Latest open access articles published in Journal of Engineering Management and Systems Engineering at https://www.acadlore.com/journals/JEMSE https://www.acadlore.com/journals/JEMSE Acadlore en Creative Commons Attribution(CC - BY) JEMSE support@acadlore.com https://www.acadlore.com/article/JEMSE/2026_5_2/jemse050201 Compliance management in business operations is often addressed through fragmented procedures that are difficult to coordinate and evaluate in a consistent manner. This study develops a structured compliance management framework grounded in a system engineering perspective, with the aim of linking regulatory requirements to operational processes in a coherent way. The framework is constructed by organizing compliance activities into a set of interrelated components, including regulatory interpretation, process integration, monitoring mechanisms, and feedback loops. On this basis, an evaluation scheme is established to examine the consistency and effectiveness of compliance implementation across operational stages. Particular attention is given to the identification of critical control points and the interaction between compliance measures and routine business processes. The proposed framework is examined through its application to typical organizational settings, where it allows a more transparent mapping between compliance requirements and operational execution. The analysis shows that a system-based structure supports clearer identification of process dependencies and facilitates more consistent evaluation outcomes. The study provides a structured basis for understanding compliance as an integrated operational system rather than a set of isolated practices, and offers a foundation for more informed decision-making in compliance management. 04-09-2026 <![CDATA[ Compliance management in business operations is often addressed through fragmented procedures that are difficult to coordinate and evaluate in a consistent manner. This study develops a structured compliance management framework grounded in a system engineering perspective, with the aim of linking regulatory requirements to operational processes in a coherent way. The framework is constructed by organizing compliance activities into a set of interrelated components, including regulatory interpretation, process integration, monitoring mechanisms, and feedback loops. On this basis, an evaluation scheme is established to examine the consistency and effectiveness of compliance implementation across operational stages. Particular attention is given to the identification of critical control points and the interaction between compliance measures and routine business processes. The proposed framework is examined through its application to typical organizational settings, where it allows a more transparent mapping between compliance requirements and operational execution. The analysis shows that a system-based structure supports clearer identification of process dependencies and facilitates more consistent evaluation outcomes. The study provides a structured basis for understanding compliance as an integrated operational system rather than a set of isolated practices, and offers a foundation for more informed decision-making in compliance management. ]]> Design and Evaluation of a Compliance Management Framework for Business Operations: A System Engineering Perspective tímea antal róza számadó doi: 10.56578/jemse050201 Journal of Engineering Management and Systems Engineering 04-09-2026 Journal of Engineering Management and Systems Engineering 04-09-2026 2026 5 2 Article 120 10.56578/jemse050201 https://www.acadlore.com/article/JEMSE/2026_5_2/jemse050201 https://www.acadlore.com/article/JEMSE/2026_5_1/jemse050107 Hospital infrastructure systems represent one of the most complex categories of engineered systems, characterized by the tight integration of system configuration, technical subsystems, operational processes, and governance structures. Despite their structural durability, such systems—particularly in institutionally unstable environments—are prone to early functional and operational obsolescence, leading to performance degradation over the lifecycle. This challenge highlights the need to conceptualize hospitals not as static built assets, but as dynamic socio-technical systems requiring systematic performance-oriented management. This study develops a system-level analytical framework to examine future-proofing as an emergent outcome of interactions among institutional and contextual drivers, planning mechanisms, innovation, and design capabilities. The empirical analysis is conducted using data collected from professionals engaged in hospital infrastructure projects in Iraq. A Partial Least Squares Structural Equation Modeling (PLS-SEM) approach is employed to evaluate both direct and indirect relationships within the proposed system model. The results demonstrate that institutional and contextual drivers significantly influence planning mechanisms, which in turn act as a central structuring layer affecting both innovation and design capabilities. Innovation does not exhibit a statistically significant direct effect on long-term system adaptability, indicating that technological advancement alone is insufficient to ensure sustained performance. In contrast, design capabilities constitute the primary determinant of future-proofing, with a strong mediating effect on lifecycle system performance. The findings provide important implications for engineering management by emphasizing that long-term adaptability in hospital infrastructure systems depends on the alignment between planning structures and implementation-oriented design capabilities, rather than on innovation intensity alone. 03-30-2026 <![CDATA[ Hospital infrastructure systems represent one of the most complex categories of engineered systems, characterized by the tight integration of system configuration, technical subsystems, operational processes, and governance structures. Despite their structural durability, such systems—particularly in institutionally unstable environments—are prone to early functional and operational obsolescence, leading to performance degradation over the lifecycle. This challenge highlights the need to conceptualize hospitals not as static built assets, but as dynamic socio-technical systems requiring systematic performance-oriented management. This study develops a system-level analytical framework to examine future-proofing as an emergent outcome of interactions among institutional and contextual drivers, planning mechanisms, innovation, and design capabilities. The empirical analysis is conducted using data collected from professionals engaged in hospital infrastructure projects in Iraq. A Partial Least Squares Structural Equation Modeling (PLS-SEM) approach is employed to evaluate both direct and indirect relationships within the proposed system model. The results demonstrate that institutional and contextual drivers significantly influence planning mechanisms, which in turn act as a central structuring layer affecting both innovation and design capabilities. Innovation does not exhibit a statistically significant direct effect on long-term system adaptability, indicating that technological advancement alone is insufficient to ensure sustained performance. In contrast, design capabilities constitute the primary determinant of future-proofing, with a strong mediating effect on lifecycle system performance. The findings provide important implications for engineering management by emphasizing that long-term adaptability in hospital infrastructure systems depends on the alignment between planning structures and implementation-oriented design capabilities, rather than on innovation intensity alone. ]]> Future-Proofing in Hospital Infrastructure Systems: A Partial Least Squares Structural Equation Modeling Analysis of Institutional Drivers, Planning Mechanisms, and Design Capabilities mustafa jawad kadhum enas salim abdulahad amjad zaki khalil al-musaed doi: 10.56578/jemse050107 Journal of Engineering Management and Systems Engineering 03-30-2026 Journal of Engineering Management and Systems Engineering 03-30-2026 2026 5 1 Article 101 10.56578/jemse050107 https://www.acadlore.com/article/JEMSE/2026_5_1/jemse050107 https://www.acadlore.com/article/JEMSE/2026_5_1/jemse050106 Electronics and mechatronics have transformed railway vehicles into complex cyber–physical systems integrating mechanical assemblies, electrical architectures, and embedded software across multi-decade lifecycles. Maintaining configuration integrity across these domains is particularly challenging under phased retrofits, supplier substitutions, and stringent safety requirements. This paper presents a structured narrative review of Product Lifecycle Management (PLM)-enabled approaches for governing electronics and mechatronics lifecycle data in railway manufacturing. Guided by explicit research questions, the review synthesizes literature from 2010–2025 using transparent search and inclusion criteria aligned with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting principles. Drawing on systems engineering, lifecycle management, and configuration management theory, the analysis examines multi-domain BOM governance, hardware–software traceability, variant and effectivity control, digital thread continuity, and supplier collaboration. Cross-industry implementation archetypes are evaluated to clarify contextual boundary conditions and transferability limits. The findings indicate that configuration-centric governance—rather than tool integration alone—is the primary determinant of PLM effectiveness in long-lifecycle, safety-regulated environments. A structured future research agenda and explicit engineering management implications are proposed to strengthen digital continuity, fleet-level effectivity discipline, and safety-aligned lifecycle governance. 03-28-2026 <![CDATA[ Electronics and mechatronics have transformed railway vehicles into complex cyber–physical systems integrating mechanical assemblies, electrical architectures, and embedded software across multi-decade lifecycles. Maintaining configuration integrity across these domains is particularly challenging under phased retrofits, supplier substitutions, and stringent safety requirements. This paper presents a structured narrative review of Product Lifecycle Management (PLM)-enabled approaches for governing electronics and mechatronics lifecycle data in railway manufacturing. Guided by explicit research questions, the review synthesizes literature from 2010–2025 using transparent search and inclusion criteria aligned with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting principles. Drawing on systems engineering, lifecycle management, and configuration management theory, the analysis examines multi-domain BOM governance, hardware–software traceability, variant and effectivity control, digital thread continuity, and supplier collaboration. Cross-industry implementation archetypes are evaluated to clarify contextual boundary conditions and transferability limits. The findings indicate that configuration-centric governance—rather than tool integration alone—is the primary determinant of PLM effectiveness in long-lifecycle, safety-regulated environments. A structured future research agenda and explicit engineering management implications are proposed to strengthen digital continuity, fleet-level effectivity discipline, and safety-aligned lifecycle governance. ]]> Electronics/Mechatronics Data Management in Railway Manufacturing: A Review of Product Lifecycle Management-based Strategies for Digital Continuity and Lifecycle Configuration Control prasun saha doi: 10.56578/jemse050106 Journal of Engineering Management and Systems Engineering 03-28-2026 Journal of Engineering Management and Systems Engineering 03-28-2026 2026 5 1 Article 85 10.56578/jemse050106 https://www.acadlore.com/article/JEMSE/2026_5_1/jemse050106 https://www.acadlore.com/article/JEMSE/2026_5_1/jemse050105 Assessing sustainable development performance is essential for understanding national progress. This study evaluates the sustainable development performance of G20 member countries (excluding the EU) using an integrated set of economic, social, and environmental indicators. Economic performance is captured by GDP per capita and the unemployment rate, while social performance is assessed through the education index, health expenditure, and income inequality (Gini coefficient). Environmental performance is represented by CO$_2$ emissions and the share of renewable energy in total energy consumption. Objective, data-driven weights were derived using the Criteria Importance Through Intercriteria Correlation (CRITIC) method, and countries were subsequently ranked using two multi-criteria decision-making (MCDM) approaches: Additive Ratio Assessment (ARAS) and Grey Relational Analysis (GRA). Correlation analysis (Spearman, Kendall, and Pearson) was conducted to examine the consistency and reliability of the rankings. The findings provide a comparative assessment of sustainability performance across G20 countries, highlighting relative strengths and weaknesses across economic, social, and environmental dimensions. The results offer a structured reference for decision-makers in engineering management and policy design in formulating evidence-based strategies. 03-27-2026 <![CDATA[ Assessing sustainable development performance is essential for understanding national progress. This study evaluates the sustainable development performance of G20 member countries (excluding the EU) using an integrated set of economic, social, and environmental indicators. Economic performance is captured by GDP per capita and the unemployment rate, while social performance is assessed through the education index, health expenditure, and income inequality (Gini coefficient). Environmental performance is represented by CO$_2$ emissions and the share of renewable energy in total energy consumption. Objective, data-driven weights were derived using the Criteria Importance Through Intercriteria Correlation (CRITIC) method, and countries were subsequently ranked using two multi-criteria decision-making (MCDM) approaches: Additive Ratio Assessment (ARAS) and Grey Relational Analysis (GRA). Correlation analysis (Spearman, Kendall, and Pearson) was conducted to examine the consistency and reliability of the rankings. The findings provide a comparative assessment of sustainability performance across G20 countries, highlighting relative strengths and weaknesses across economic, social, and environmental dimensions. The results offer a structured reference for decision-makers in engineering management and policy design in formulating evidence-based strategies. ]]> Evaluating the Sustainable Development Performance of G20 Economies Using an Integrated Decision-Making Framework beyzanur cayir ervural doi: 10.56578/jemse050105 Journal of Engineering Management and Systems Engineering 03-27-2026 Journal of Engineering Management and Systems Engineering 03-27-2026 2026 5 1 Article 63 10.56578/jemse050105 https://www.acadlore.com/article/JEMSE/2026_5_1/jemse050105 https://www.acadlore.com/article/JEMSE/2026_5_1/jemse050104 This study proposes a fuzzy-based adaptive temperature management system for hydroponic cultivation in tropical climates. Unlike conventional fixed-setpoint controllers, the proposed Mamdani fuzzy system utilizes pH and electrical conductivity (EC) as contextual inputs to dynamically adjust temperature control strategies. The underlying hypothesis is that maintaining lower root-zone temperatures (RZTs) during suboptimal pH/EC conditions may increase dissolved oxygen availability, partially compensating for nutrient stress. The Internet of Things (IoT)-enabled system employs Long Range wireless protocol (LoRa) communication for long-range, low-power data transmission, with fuzzy inference executed at the gateway for offline resilience. A five-month field validation (April–August 2024) in Ho Chi Minh City demonstrated effective temperature regulation, maintaining solution temperature within the 18–28 °C operational target range for 88.7% of the trial period, with zero exceedance of the 35 °C critical threshold. The system maintained pH at 5.72 ± 0.32 (86.4% time in optimal range) and EC at 1.87 ± 0.28 mS/cm (81.3% time in optimal range). Retrospective simulation comparing the proposed controller against On/Off, proportional-integral (PI) baselines, and temperature-only FLC baselines, demonstrated a 15–16% reduction in chiller runtime while maintaining equivalent thermal safety. Operational crop assessment across three cultivation cycles indicated commercially viable lettuce production. A dedicated system engineering analysis addresses architecture trade-offs, reliability, scalability, and cost-effectiveness for practical deployment in tropical commercial operations. 03-26-2026 <![CDATA[ <p>This study proposes a fuzzy-based adaptive temperature management system for hydroponic cultivation in tropical climates. Unlike conventional fixed-setpoint controllers, the proposed Mamdani fuzzy system utilizes pH and electrical conductivity (EC) as contextual inputs to dynamically adjust temperature control strategies. The underlying hypothesis is that maintaining lower root-zone temperatures (RZTs) during suboptimal pH/EC conditions may increase dissolved oxygen availability, partially compensating for nutrient stress. The Internet of Things (IoT)-enabled system employs Long Range wireless protocol (LoRa) communication for long-range, low-power data transmission, with fuzzy inference executed at the gateway for offline resilience. A five-month field validation (April–August 2024) in Ho Chi Minh City demonstrated effective temperature regulation, maintaining solution temperature within the 18–28 °C operational target range for 88.7% of the trial period, with zero exceedance of the 35 °C critical threshold. The system maintained pH at 5.72 ± 0.32 (86.4% time in optimal range) and EC at 1.87 ± 0.28 mS/cm (81.3% time in optimal range). Retrospective simulation comparing the proposed controller against On/Off, proportional-integral (PI) baselines, and temperature-only FLC baselines, demonstrated a 15–16% reduction in chiller runtime while maintaining equivalent thermal safety. Operational crop assessment across three cultivation cycles indicated commercially viable lettuce production. A dedicated system engineering analysis addresses architecture trade-offs, reliability, scalability, and cost-effectiveness for practical deployment in tropical commercial operations.</p> ]]> Fuzzy-Based Adaptive Temperature Management for Hydroponic Systems: An IoT-Enabled Approach Considering Nutrient Solution Dynamics tran thanh trang tran huu khoa tran nhut tam doi: 10.56578/jemse050104 Journal of Engineering Management and Systems Engineering 03-26-2026 Journal of Engineering Management and Systems Engineering 03-26-2026 2026 5 1 Article 42 10.56578/jemse050104 https://www.acadlore.com/article/JEMSE/2026_5_1/jemse050104 https://www.acadlore.com/article/JEMSE/2026_5_1/jemse050103 This study explores the macroeconomic impact of the Zero Over-Dimension Over-Load (ODOL) policy in Indonesia, especially its influence on logistics costs, inflation, and economic growth. The policy is not discussed here only as a matter of transport compliance, but also as a structural change in logistics governance that may affect the wider economy. A mixed-methods approach was used, based on primary survey data collected in 2025 from logistics stakeholders in DKI Jakarta and West Java. For the analysis, the Leontief Price Model was applied to estimate price transmission effects, while the dynamic Computable General Equilibrium (CGE) IndoTERM model was used to simulate cost shocks, investment adjustment, and fiscal reallocation. The findings show that the policy increases national logistics costs by 4.58% in the short term, which raises the logistics cost-to-GDP ratio to 14.94%. However, the longer-term results are more positive. The simulation suggests a 0.05% increase in GDP, equivalent to a net output gain of IDR 14.3 trillion. This result is associated with a 6.74% increase in fleet investment, estimated at IDR 42.4 trillion, as well as fiscal savings caused by lower infrastructure damage. These results suggest that stricter logistics regulation may bring broader economic benefits when the analysis goes beyond the immediate rise in transport costs. In practical terms, the policy should be supported by fiscal incentives for fleet modernization and by careful timing of enforcement, especially to limit inflationary pressure in food and construction-related sectors. 03-26-2026 <![CDATA[ <p>This study explores the macroeconomic impact of the Zero Over-Dimension Over-Load (ODOL) policy in Indonesia, especially its influence on logistics costs, inflation, and economic growth. The policy is not discussed here only as a matter of transport compliance, but also as a structural change in logistics governance that may affect the wider economy. A mixed-methods approach was used, based on primary survey data collected in 2025 from logistics stakeholders in DKI Jakarta and West Java. For the analysis, the Leontief Price Model was applied to estimate price transmission effects, while the dynamic Computable General Equilibrium (CGE) IndoTERM model was used to simulate cost shocks, investment adjustment, and fiscal reallocation. The findings show that the policy increases national logistics costs by 4.58% in the short term, which raises the logistics cost-to-GDP ratio to 14.94%. However, the longer-term results are more positive. The simulation suggests a 0.05% increase in GDP, equivalent to a net output gain of IDR 14.3 trillion. This result is associated with a 6.74% increase in fleet investment, estimated at IDR 42.4 trillion, as well as fiscal savings caused by lower infrastructure damage. These results suggest that stricter logistics regulation may bring broader economic benefits when the analysis goes beyond the immediate rise in transport costs. In practical terms, the policy should be supported by fiscal incentives for fleet modernization and by careful timing of enforcement, especially to limit inflationary pressure in food and construction-related sectors.</p> ]]> Modeling the Economic Impacts of Indonesia’s Zero Over-Dimension Over-Load Policy: A Logistics System Governance Perspective ilham ilham doi: 10.56578/jemse050103 Journal of Engineering Management and Systems Engineering 03-26-2026 Journal of Engineering Management and Systems Engineering 03-26-2026 2026 5 1 Article 33 10.56578/jemse050103 https://www.acadlore.com/article/JEMSE/2026_5_1/jemse050103 https://www.acadlore.com/article/JEMSE/2026_5_1/jemse050102 Remote sensing plays a crucial role in disaster management. Moreover, its effectiveness is severely limited due to operational, technological and environmental challenges. Data acquisition can be disrupted by sensor limitations and by extreme events or natural factors, such as cloud cover. In fact, high-resolution imagery often requires significant processing time, specialized expertise and expensive infrastructure. Therefore, ensuring timely, accurate and accessible remote sensing data at all stages—preparedness, response, recovery and mitigation—is a major challenge. This study explores the application of multi-criteria decision making (MCDM) techniques using bipolar fuzzy numbers (BFNs) to evaluate this. We apply the weighted and ranking MCDM method, i.e., Method Based on the Removal Effects of Criteria (MEREC) and Multi-Attributive Border Approximation Area Comparison (MABAC), respectively, in this paper. The decisions of multiple decision makers (DMs) are considered when collecting this problem related data and BFNs are utilised as mathematical tools to handle uncertainty. In order to address the ambiguity and inconsistency of the system, we finally conclude to conduct the comparative and sensitivity analyses here with the final result. 01-15-2026 <![CDATA[ Remote sensing plays a crucial role in disaster management. Moreover, its effectiveness is severely limited due to operational, technological and environmental challenges. Data acquisition can be disrupted by sensor limitations and by extreme events or natural factors, such as cloud cover. In fact, high-resolution imagery often requires significant processing time, specialized expertise and expensive infrastructure. Therefore, ensuring timely, accurate and accessible remote sensing data at all stages—preparedness, response, recovery and mitigation—is a major challenge. This study explores the application of multi-criteria decision making (MCDM) techniques using bipolar fuzzy numbers (BFNs) to evaluate this. We apply the weighted and ranking MCDM method, i.e., Method Based on the Removal Effects of Criteria (MEREC) and Multi-Attributive Border Approximation Area Comparison (MABAC), respectively, in this paper. The decisions of multiple decision makers (DMs) are considered when collecting this problem related data and BFNs are utilised as mathematical tools to handle uncertainty. In order to address the ambiguity and inconsistency of the system, we finally conclude to conduct the comparative and sensitivity analyses here with the final result. ]]> Challenges of Remote Sensing for Crucial Role in All Phases of Disaster Management: An Uncertain MCDM-Based Study kamal hossain gazi aditi biswas sankar prasad mondal arijit ghosh doi: 10.56578/jemse050102 Journal of Engineering Management and Systems Engineering 01-15-2026 Journal of Engineering Management and Systems Engineering 01-15-2026 2026 5 1 Article 10 10.56578/jemse050102 https://www.acadlore.com/article/JEMSE/2026_5_1/jemse050102 https://www.acadlore.com/article/JEMSE/2026_5_1/jemse050101 Efficient management of production processes in modern manufacturing depends on the timely identification of their most critical phases, as such recognition directly enhances process reliability, productivity, and product quality. To address this need, an objective multi-attribute decision-making (MADM) framework has been developed by integrating the Criteria Importance Through Inter-criteria Correlation (CRITIC) method with Pareto analysis, a well-established approach also referred to as ABC classification. Within this framework, a comprehensive set of evaluation criteria was determined in collaboration with a Process Failure Mode and Effects Analysis (PFMEA) team from a Tier-1 automotive manufacturer. The decision matrix was constructed from data extracted from PFMEA reports that had been subjected to preliminary statistical processing to ensure robustness and comparability. The relative importance of the criteria was then established using the CRITIC method, which objectively derives weights from statistical indicators such as the arithmetic mean, standard deviation, and inter-criteria correlation coefficients. The framework was subsequently applied to the PFMEA report for a rear axle assembly process, encompassing 16 discrete production phases. Pareto analysis was employed to classify the phases according to their criticality, thereby enabling a systematic prioritization of process risks. The resulting classification demonstrated strong consistency with expert evaluations and was confirmed to reflect real-world production conditions accurately. Beyond confirming methodological validity, the findings underscore the advantages of employing a fully objective weighting mechanism combined with a widely recognized prioritization tool, thereby offering a transparent and replicable basis for decision-making in complex manufacturing contexts. This integration not only supports continuous improvement and risk mitigation but also provides a scalable framework applicable to a broad range of industrial processes where critical phase identification is essential. 12-29-2025 <![CDATA[ Efficient management of production processes in modern manufacturing depends on the timely identification of their most critical phases, as such recognition directly enhances process reliability, productivity, and product quality. To address this need, an objective multi-attribute decision-making (MADM) framework has been developed by integrating the Criteria Importance Through Inter-criteria Correlation (CRITIC) method with Pareto analysis, a well-established approach also referred to as ABC classification. Within this framework, a comprehensive set of evaluation criteria was determined in collaboration with a Process Failure Mode and Effects Analysis (PFMEA) team from a Tier-1 automotive manufacturer. The decision matrix was constructed from data extracted from PFMEA reports that had been subjected to preliminary statistical processing to ensure robustness and comparability. The relative importance of the criteria was then established using the CRITIC method, which objectively derives weights from statistical indicators such as the arithmetic mean, standard deviation, and inter-criteria correlation coefficients. The framework was subsequently applied to the PFMEA report for a rear axle assembly process, encompassing 16 discrete production phases. Pareto analysis was employed to classify the phases according to their criticality, thereby enabling a systematic prioritization of process risks. The resulting classification demonstrated strong consistency with expert evaluations and was confirmed to reflect real-world production conditions accurately. Beyond confirming methodological validity, the findings underscore the advantages of employing a fully objective weighting mechanism combined with a widely recognized prioritization tool, thereby offering a transparent and replicable basis for decision-making in complex manufacturing contexts. This integration not only supports continuous improvement and risk mitigation but also provides a scalable framework applicable to a broad range of industrial processes where critical phase identification is essential. ]]> An Objective Multi-Attribute Decision-Making Framework for Identifying Critical Phases in Automotive Component Manufacturing nikola komatina dragan marinković doi: 10.56578/jemse050101 Journal of Engineering Management and Systems Engineering 12-29-2025 Journal of Engineering Management and Systems Engineering 12-29-2025 2026 5 1 Article 1 10.56578/jemse050101 https://www.acadlore.com/article/JEMSE/2026_5_1/jemse050101 https://www.acadlore.com/article/JEMSE/2025_4_4/jemse040405 The rapid urbanization and economic development in China have led to increasing demand for infrastructure systems such as utilities, water, gas, and communication networks, exacerbating urban challenges like land scarcity and congestion. Previous studies have highlighted the potential of underground space development as a means to address these issues. Underground utility tunnel construction has been identified as a key solution for efficient pipeline maintenance and the advancement of smart city initiatives. However, as the scale of such projects continues to grow, so does the associated risk. Traditional risk assessment frameworks have often overlooked the significance of intelligent operation and maintenance (O&amp;M) in the context of the digital transformation of infrastructure. This study proposes an updated risk assessment approach that integrates smart O&amp;M into the evaluation framework, reflecting the adoption of technologies such as Building Information Modeling (BIM), digital twins, and big data in construction processes. The Analytic Hierarchy Process (AHP), expert consultations, questionnaire surveys, and fuzzy evaluation methods are applied to identify and assess risks in an underground utility tunnel project in Q City. The results indicate that the overall risk level of the project is above average, with the most significant risks occurring during the construction and operational phases. Risk mitigation measures have been proposed for the identified high-risk areas, tailored to the specific characteristics of the project. This study underscores the importance of incorporating smart operation and information technology risks into traditional risk management frameworks. The findings emphasize the need for a paradigm shift in the risk management of underground utility tunnel projects, particularly in light of the ongoing digital transformation of infrastructure. Such an approach would enhance the safety and efficiency of project management across the entire life cycle of the tunnel system. 12-22-2025 <![CDATA[ The rapid urbanization and economic development in China have led to increasing demand for infrastructure systems such as utilities, water, gas, and communication networks, exacerbating urban challenges like land scarcity and congestion. Previous studies have highlighted the potential of underground space development as a means to address these issues. Underground utility tunnel construction has been identified as a key solution for efficient pipeline maintenance and the advancement of smart city initiatives. However, as the scale of such projects continues to grow, so does the associated risk. Traditional risk assessment frameworks have often overlooked the significance of intelligent operation and maintenance (O&amp;M) in the context of the digital transformation of infrastructure. This study proposes an updated risk assessment approach that integrates smart O&amp;M into the evaluation framework, reflecting the adoption of technologies such as Building Information Modeling (BIM), digital twins, and big data in construction processes. The Analytic Hierarchy Process (AHP), expert consultations, questionnaire surveys, and fuzzy evaluation methods are applied to identify and assess risks in an underground utility tunnel project in Q City. The results indicate that the overall risk level of the project is above average, with the most significant risks occurring during the construction and operational phases. Risk mitigation measures have been proposed for the identified high-risk areas, tailored to the specific characteristics of the project. This study underscores the importance of incorporating smart operation and information technology risks into traditional risk management frameworks. The findings emphasize the need for a paradigm shift in the risk management of underground utility tunnel projects, particularly in light of the ongoing digital transformation of infrastructure. Such an approach would enhance the safety and efficiency of project management across the entire life cycle of the tunnel system. ]]> Risk Assessment of Underground Utility Tunnel Projects in Q City Using the Analytic Hierarchy Process feiyang ou xiaoning zhu qunxia li rui yan doi: 10.56578/jemse040405 Journal of Engineering Management and Systems Engineering 12-22-2025 Journal of Engineering Management and Systems Engineering 12-22-2025 2025 4 4 Article 284 10.56578/jemse040405 https://www.acadlore.com/article/JEMSE/2025_4_4/jemse040405 https://www.acadlore.com/article/JEMSE/2025_4_4/jemse040404 The rapid growth of global trade has heightened the importance of efficient container handling, environmentally responsible operations, and high-performing equipment selection in sustaining the competitiveness of modern supply chains. Container Freight Stations (CFS) serve as critical operational hubs where loading, unloading, inspection, and temporary storage activities are conducted, thereby requiring equipment capable of safely and efficiently handling heavy-tonnage cargo while aligning with green port transformation goals. Forklifts, which constitute one of the core equipment groups in CFS yards, differ significantly in terms of lifting capacity, power systems, maneuverability, hydraulic performance, ergonomics, and environmental impact, transforming forklift selection into a complex, multi-dimensional decision problem shaped by both technical and Environmental, Social, and Governance (ESG)-oriented considerations. Incorrect equipment choices may lead to operational downtime, energy inefficiency, equipment failures, and occupational safety risks, particularly in operations involving loads exceeding 25 tons. To address these challenges, this study proposes a hybrid decision-making framework that integrates expert-driven fuzzy assessments with sustainability-based evaluation using the FF-Hamacher-MEREC-ARLON methodology. In the first stage, expert weights and criterion importance values were calculated through the FF-MEREC approach, while alternative forklifts were ranked using the FF-ARLON method in the second stage. Two sensitivity analysis scenarios were applied: one by modifying the tradeoff ratio within ARLON and the other by sequentially removing each criterion. In both scenarios, the fourth alternative consistently emerged as the most suitable option. Furthermore, comparative analyses using eight established MCDM techniques; ALWAS, AROMAN, ARTASI, MABAC, MARCOS, RAM, SAW, and WASPAS; demonstrated complete agreement with the proposed model, confirming the fourth alternative as the top-ranked choice. The findings highlight the robustness, reliability, and sustainability alignment of the proposed framework for high-stakes heavy-duty equipment selection in port-based logistics operations. 11-27-2025 <![CDATA[ <p>The rapid growth of global trade has heightened the importance of efficient container handling, environmentally responsible operations, and high-performing equipment selection in sustaining the competitiveness of modern supply chains. Container Freight Stations (CFS) serve as critical operational hubs where loading, unloading, inspection, and temporary storage activities are conducted, thereby requiring equipment capable of safely and efficiently handling heavy-tonnage cargo while aligning with green port transformation goals. Forklifts, which constitute one of the core equipment groups in CFS yards, differ significantly in terms of lifting capacity, power systems, maneuverability, hydraulic performance, ergonomics, and environmental impact, transforming forklift selection into a complex, multi-dimensional decision problem shaped by both technical and Environmental, Social, and Governance <span style="color: rgb(0, 0, 0); font-family: Times New Roman, sans-serif">(</span>ESG)-oriented considerations. Incorrect equipment choices may lead to operational downtime, energy inefficiency, equipment failures, and occupational safety risks, particularly in operations involving loads exceeding 25 tons. To address these challenges, this study proposes a hybrid decision-making framework that integrates expert-driven fuzzy assessments with sustainability-based evaluation using the FF-Hamacher-MEREC-ARLON methodology. In the first stage, expert weights and criterion importance values were calculated through the FF-MEREC approach, while alternative forklifts were ranked using the FF-ARLON method in the second stage. Two sensitivity analysis scenarios were applied: one by modifying the tradeoff ratio within ARLON and the other by sequentially removing each criterion. In both scenarios, the fourth alternative consistently emerged as the most suitable option. Furthermore, comparative analyses using eight established MCDM techniques; ALWAS, AROMAN, ARTASI, MABAC, MARCOS, RAM, SAW, and WASPAS; demonstrated complete agreement with the proposed model, confirming the fourth alternative as the top-ranked choice. The findings highlight the robustness, reliability, and sustainability alignment of the proposed framework for high-stakes heavy-duty equipment selection in port-based logistics operations.</p> ]]> A Fermatean Fuzzy MCDM Framework for Green Port Transformation and Heavy-Duty Forklift Selection galip cihan yalçın karahan kara pınar gürol matej babič doi: 10.56578/jemse040404 Journal of Engineering Management and Systems Engineering 11-27-2025 Journal of Engineering Management and Systems Engineering 11-27-2025 2025 4 4 Article 269 10.56578/jemse040404 https://www.acadlore.com/article/JEMSE/2025_4_4/jemse040404 https://www.acadlore.com/article/JEMSE/2025_4_4/jemse040403 The Load Haul Dumper (LHD) is essential machinery utilized for moving ore in the underground mining industry, in order to fulfil production targets. In this connection, the efficiency of the equipment should be maintained at an ideal standard, to be accomplished by reducing unexpected failure of components or subsystems in this intricate system. Downtime analysis helped identify faulty components and subsystems, which require the development of complementary maintenance plans to facilitate the replacement or fixing of parts. Proper practices of maintenance management improve the performance of the equipment. In this research, the efficiency of the LHD machine was assessed through reliability methods. Initially, the assumption of independent and identical distribution (IID) for the data sets was validated using trend and serial correlation analyses. The statistical tests indicated that the data sets adhered to the IID assumption. Therefore, a renewal process method was utilized for additional examination. The Kolmogorov-Smirnov (K-S) test was utilized to identify the most suitable distribution for the data sets. The theoretical probability distributions were estimated parametrically using the Maximum Likelihood Estimate (MLE) approach. The dependability of each separate subsystem was determined using the optimal fit distribution. Based on the reliability outcomes, preventive maintenance (PM) time plans were created to reach the targeted 90% reliability. Different maintenance strategies, in addition, were suggested to the maintenance team to extend the lifespan of the machine. 11-20-2025 <![CDATA[ <p>The Load Haul Dumper (LHD) is essential machinery utilized for moving ore in the underground mining industry, in order to fulfil production targets. In this connection, the efficiency of the equipment should be maintained at an ideal standard, to be accomplished by reducing unexpected failure of components or subsystems in this intricate system. Downtime analysis helped identify faulty components and subsystems, which require the development of complementary maintenance plans to facilitate the replacement or fixing of parts. Proper practices of maintenance management improve the performance of the equipment. In this research, the efficiency of the LHD machine was assessed through reliability methods. Initially, the assumption of independent and identical distribution (IID) for the data sets was validated using trend and serial correlation analyses. The statistical tests indicated that the data sets adhered to the IID assumption. Therefore, a renewal process method was utilized for additional examination. The Kolmogorov-Smirnov (K-S) test was utilized to identify the most suitable distribution for the data sets. The theoretical probability distributions were estimated parametrically using the Maximum Likelihood Estimate (MLE) approach. The dependability of each separate subsystem was determined using the optimal fit distribution. Based on the reliability outcomes, preventive maintenance (PM) time plans were created to reach the targeted 90% reliability. Different maintenance strategies, in addition, were suggested to the maintenance team to extend the lifespan of the machine.</p> ]]> Effective Maintenance Planning for Improving the Reliability of Underground Mining Equipment—A Case Study balaraju jakkula govinda raj mandela anup kumar tripathi doi: 10.56578/jemse040403 Journal of Engineering Management and Systems Engineering 11-20-2025 Journal of Engineering Management and Systems Engineering 11-20-2025 2025 4 4 Article 257 10.56578/jemse040403 https://www.acadlore.com/article/JEMSE/2025_4_4/jemse040403 https://www.acadlore.com/article/JEMSE/2025_4_4/jemse040402 Operations managers and engineers in the automotive industry confront the key challenge in ensuring the reliability of the manufacturing process. To accurately classify failure modes, this study proposed a novel Multi-Criteria Decision-Making (MCDM) model integrated with Single-Valued Neutrosophic Sets (SVNSs) for operations management to prioritize actions in eliminating failure modes that had the greatest impact on the concerned reliability. The identification and evaluation of failure modes were grounded in the conventional Failure Mode and Effect Analysis (FMEA), while the relative importance of risk factors (RFs) was expressed through predefined linguistic terms modelled with the SVNSs. The assessment of these risk factors was formulated as a fuzzy group decision-making problem and the fuzzy weight vector was derived from the Order Weighted Averaging (OWA) operator. Failure rankings were conducted through a modified version of the Elimination and Choice Translating Reality (ELECTRE) method; being tested and validated with real-world data from an automotive company, the proposed FMEA-ELECTRE model could inspire stakeholders in various industries to explore this scientific contribution further. 09-08-2025 <![CDATA[ Operations managers and engineers in the automotive industry confront the key challenge in ensuring the reliability of the manufacturing process. To accurately classify failure modes, this study proposed a novel Multi-Criteria Decision-Making (MCDM) model integrated with Single-Valued Neutrosophic Sets (SVNSs) for operations management to prioritize actions in eliminating failure modes that had the greatest impact on the concerned reliability. The identification and evaluation of failure modes were grounded in the conventional Failure Mode and Effect Analysis (FMEA), while the relative importance of risk factors (RFs) was expressed through predefined linguistic terms modelled with the SVNSs. The assessment of these risk factors was formulated as a fuzzy group decision-making problem and the fuzzy weight vector was derived from the Order Weighted Averaging (OWA) operator. Failure rankings were conducted through a modified version of the Elimination and Choice Translating Reality (ELECTRE) method; being tested and validated with real-world data from an automotive company, the proposed FMEA-ELECTRE model could inspire stakeholders in various industries to explore this scientific contribution further. ]]> Neutrosophic Failure Mode and Effect Analysis—Elimination and Choice Translating Reality Method for Prioritizing Failure Modes marija savković vladan paunović carlo caiazzo nikola komatina doi: 10.56578/jemse040402 Journal of Engineering Management and Systems Engineering 09-08-2025 Journal of Engineering Management and Systems Engineering 09-08-2025 2025 4 4 Article 245 10.56578/jemse040402 https://www.acadlore.com/article/JEMSE/2025_4_4/jemse040402 https://www.acadlore.com/article/JEMSE/2025_4_4/jemse040401 In an increasingly dynamic and complex industrial landscape, the continuous enhancement of organizational performance has emerged as a critical imperative. To this end, structured quality assessment frameworks, such as the European Foundation for Quality Management (EFQM) Excellence Model, have been widely adopted as integrative tools for diagnosing, monitoring, and improving business performance. Despite its comprehensive nature, the EFQM model often requires the incorporation of additional quantitative methods to refine the evaluation of the relative significance of its criteria. In this study, the Analytic Hierarchy Process (AHP) method, extended with triangular fuzzy numbers, has been employed to determine the weighted importance of the EFQM model's criteria under conditions of uncertainty and expert subjectivity. This fuzzy extension of AHP allows for a more nuanced capture of linguistic judgments, thereby enhancing the robustness of decision-making in ambiguous environments. Expert assessments were elicited through structured interviews with quality managers from three manufacturing companies, enabling the construction of pairwise comparison matrices for each criterion. These matrices were then aggregated and analyzed to derive consensus-based priority weights. The findings reveal significant variations in the perceived importance of enabler and result criteria, underscoring the context-dependent applicability of the EFQM model. Furthermore, the results offer a more granular understanding of the internal structure of the model, providing a foundation for its adaptive use in quality management systems across the manufacturing sector. The integration of fuzzy logic into the hierarchical decision-making process is demonstrated to yield improved precision and flexibility, making it a valuable methodological enhancement for organizations pursuing excellence under uncertainty. The proposed approach also contributes to the broader discourse on multi-criteria decision analysis in quality management by addressing limitations in conventional crisp AHP applications. 08-19-2025 <![CDATA[ In an increasingly dynamic and complex industrial landscape, the continuous enhancement of organizational performance has emerged as a critical imperative. To this end, structured quality assessment frameworks, such as the European Foundation for Quality Management (EFQM) Excellence Model, have been widely adopted as integrative tools for diagnosing, monitoring, and improving business performance. Despite its comprehensive nature, the EFQM model often requires the incorporation of additional quantitative methods to refine the evaluation of the relative significance of its criteria. In this study, the Analytic Hierarchy Process (AHP) method, extended with triangular fuzzy numbers, has been employed to determine the weighted importance of the EFQM model's criteria under conditions of uncertainty and expert subjectivity. This fuzzy extension of AHP allows for a more nuanced capture of linguistic judgments, thereby enhancing the robustness of decision-making in ambiguous environments. Expert assessments were elicited through structured interviews with quality managers from three manufacturing companies, enabling the construction of pairwise comparison matrices for each criterion. These matrices were then aggregated and analyzed to derive consensus-based priority weights. The findings reveal significant variations in the perceived importance of enabler and result criteria, underscoring the context-dependent applicability of the EFQM model. Furthermore, the results offer a more granular understanding of the internal structure of the model, providing a foundation for its adaptive use in quality management systems across the manufacturing sector. The integration of fuzzy logic into the hierarchical decision-making process is demonstrated to yield improved precision and flexibility, making it a valuable methodological enhancement for organizations pursuing excellence under uncertainty. The proposed approach also contributes to the broader discourse on multi-criteria decision analysis in quality management by addressing limitations in conventional crisp AHP applications. ]]> Prioritization of EFQM Excellence Model Criteria Using a Fuzzy AHP Approach with Triangular Fuzzy Numbers in the Manufacturing Sector tijana petrović oliver momčilović slađana vujičić doi: 10.56578/jemse040401 Journal of Engineering Management and Systems Engineering 08-19-2025 Journal of Engineering Management and Systems Engineering 08-19-2025 2025 4 4 Article 237 10.56578/jemse040401 https://www.acadlore.com/article/JEMSE/2025_4_4/jemse040401 https://www.acadlore.com/article/JEMSE/2025_4_3/jemse040305 Inefficiencies in traditional spare parts management for aircraft maintenance—including excessive inventory costs, supply chain delays, and operational disruptions—have long hindered fleet readiness and increased maintenance expenditure. To address these challenges, an integrated, reliability-driven inventory optimization framework has been developed by leveraging predictive analytics, real-time sensor data, and emerging digital technologies. The proposed model is grounded in Reliability-Centered Maintenance (RCM) principles and enhanced by Artificial Intelligence (AI), the Internet of Things (IoT), and digital twin technologies. Through the deployment of advanced sensor networks, real-time performance data are continuously collected and analyzed to forecast component degradation and predict imminent failures. This enables the transition from time-based to condition-based maintenance scheduling. Predictive models, including Long Short-Term Memory (LSTM) neural networks and Random Forest classifiers, are employed to enhance the accuracy of failure prognostics and spare parts demand forecasting. The dynamic alignment of spare parts provisioning with actual equipment reliability has been shown to reduce overstocking and prevent critical shortages. A case study conducted within a commercial airline fleet demonstrated a 20% reduction in inventory-related costs and a 15% decrease in aircraft downtime. Furthermore, operational efficiency and safety were significantly improved by minimizing unscheduled maintenance events. The proposed framework not only supports predictive and prescriptive maintenance strategies but also establishes a replicable model for digital transformation in aviation logistics. By integrating real-time analytics with digital twin simulations, a data-centric paradigm is introduced for proactive maintenance decision-making. This advancement paves the way towards more sustainable, cost-effective, and resilient aviation operations, aligning with broader industry goals of environmental responsibility and performance optimization. 08-03-2025 <![CDATA[ <p>Inefficiencies in traditional spare parts management for aircraft maintenance—including excessive inventory costs, supply chain delays, and operational disruptions—have long hindered fleet readiness and increased maintenance expenditure. To address these challenges, an integrated, reliability-driven inventory optimization framework has been developed by leveraging predictive analytics, real-time sensor data, and emerging digital technologies. The proposed model is grounded in Reliability-Centered Maintenance (RCM) principles and enhanced by Artificial Intelligence (AI), the Internet of Things (IoT), and digital twin technologies. Through the deployment of advanced sensor networks, real-time performance data are continuously collected and analyzed to forecast component degradation and predict imminent failures. This enables the transition from time-based to condition-based maintenance scheduling. Predictive models, including Long Short-Term Memory (LSTM) neural networks and Random Forest classifiers, are employed to enhance the accuracy of failure prognostics and spare parts demand forecasting. The dynamic alignment of spare parts provisioning with actual equipment reliability has been shown to reduce overstocking and prevent critical shortages. A case study conducted within a commercial airline fleet demonstrated a 20% reduction in inventory-related costs and a 15% decrease in aircraft downtime. Furthermore, operational efficiency and safety were significantly improved by minimizing unscheduled maintenance events. The proposed framework not only supports predictive and prescriptive maintenance strategies but also establishes a replicable model for digital transformation in aviation logistics. By integrating real-time analytics with digital twin simulations, a data-centric paradigm is introduced for proactive maintenance decision-making. This advancement paves the way towards more sustainable, cost-effective, and resilient aviation operations, aligning with broader industry goals of environmental responsibility and performance optimization.</p> ]]> Predictive Reliability-Driven Optimization of Spare Parts Management in Aircraft Fleets Using AI, IoT, and Digital Twin Technologies mustafa a. s. mustafa doi: 10.56578/jemse040305 Journal of Engineering Management and Systems Engineering 08-03-2025 Journal of Engineering Management and Systems Engineering 08-03-2025 2025 4 3 Article 218 10.56578/jemse040305 https://www.acadlore.com/article/JEMSE/2025_4_3/jemse040305 https://www.acadlore.com/article/JEMSE/2025_4_3/jemse040304 The problem of job scheduling in parallel machine environments, where both processing times and setup times are characterized by stochastic variability, has been investigated with a focus on enhancing the efficiency of resource allocation in complex production systems. Job scheduling, as a critical component of operations research and systems engineering, plays a vital role in the optimization of large-scale, flexible manufacturing and service environments. In this study, a stochastic scheduling model has been formulated to minimize the maximum completion time (denoted as $Ct_{\textit{max}}$), under the simultaneous influence of probabilistic job durations and setup times associated with tool preparation. The problem has been addressed using two prominent metaheuristic algorithms: Genetic Algorithm (GA) and Simulated Annealing (SA). These methods were selected due to their demonstrated capacity to navigate large, non-deterministic search spaces efficiently and their adaptability to multi-constraint scheduling problems. A comparative analysis has been conducted by applying both algorithms under identical initial conditions, with algorithmic performance evaluated in terms of solution quality, computational efficiency, and robustness to input variability. The model incorporates key practical considerations, including randomized setup times which are often neglected in conventional deterministic scheduling models, thereby improving its relevance to real-world industrial settings. The formulation of the problem allows for additional constraints and objectives to be flexibly integrated in future research, including resource conflicts, machine eligibility constraints, and energy-aware scheduling. Empirical results suggest that while both algorithms are effective in deriving near-optimal schedules, notable differences exist in convergence behavior and sensitivity to parameter tuning. The findings offer critical insights into the comparative strengths of GA and SA in managing the stochastic nature of parallel machine scheduling problems. By advancing a robust metaheuristic framework that accounts for real-world uncertainties, this study contributes to the ongoing development of intelligent scheduling systems in systems engineering, manufacturing logistics, and automated production planning. 07-27-2025 <![CDATA[ <p>The problem of job scheduling in parallel machine environments, where both processing times and setup times are characterized by stochastic variability, has been investigated with a focus on enhancing the efficiency of resource allocation in complex production systems. Job scheduling, as a critical component of operations research and systems engineering, plays a vital role in the optimization of large-scale, flexible manufacturing and service environments. In this study, a stochastic scheduling model has been formulated to minimize the maximum completion time (denoted as $Ct_{\textit{max}}$), under the simultaneous influence of probabilistic job durations and setup times associated with tool preparation. The problem has been addressed using two prominent metaheuristic algorithms: Genetic Algorithm (GA) and Simulated Annealing (SA). These methods were selected due to their demonstrated capacity to navigate large, non-deterministic search spaces efficiently and their adaptability to multi-constraint scheduling problems. A comparative analysis has been conducted by applying both algorithms under identical initial conditions, with algorithmic performance evaluated in terms of solution quality, computational efficiency, and robustness to input variability. The model incorporates key practical considerations, including randomized setup times which are often neglected in conventional deterministic scheduling models, thereby improving its relevance to real-world industrial settings. The formulation of the problem allows for additional constraints and objectives to be flexibly integrated in future research, including resource conflicts, machine eligibility constraints, and energy-aware scheduling. Empirical results suggest that while both algorithms are effective in deriving near-optimal schedules, notable differences exist in convergence behavior and sensitivity to parameter tuning. The findings offer critical insights into the comparative strengths of GA and SA in managing the stochastic nature of parallel machine scheduling problems. By advancing a robust metaheuristic framework that accounts for real-world uncertainties, this study contributes to the ongoing development of intelligent scheduling systems in systems engineering, manufacturing logistics, and automated production planning.</p> ]]> Metaheuristic Optimization for Stochastic Job Scheduling in Parallel Machine Systems with Uncertain Processing and Setup Times aleksandar stanković nikola simić milica stanković doi: 10.56578/jemse040304 Journal of Engineering Management and Systems Engineering 07-27-2025 Journal of Engineering Management and Systems Engineering 07-27-2025 2025 4 3 Article 206 10.56578/jemse040304 https://www.acadlore.com/article/JEMSE/2025_4_3/jemse040304 https://www.acadlore.com/article/JEMSE/2025_4_3/jemse040303 In modern foundry operations, the reliability and operational continuity of sand molding systems are pivotal to maintaining productivity, safety, and competitive advantage. In this study, Failure Mode, Effects, and Criticality Analysis (FMECA) has been employed to systematically evaluate and optimize the performance of a pneumatic molding cell utilized in the production of sand molds. Particular focus has been directed toward the pusher subsystem, which is frequently subjected to high mechanical loads and cyclic stress, rendering it susceptible to recurrent failures that compromise both uptime and process efficiency. Potential failure modes were exhaustively identified, categorized, and prioritized based on their severity, occurrence, and detectability. Critical components, including servo motors, pneumatic actuators, and gearbox assemblies, were found to pose substantial risk to system reliability due to wear-induced degradation, misalignment, and lubrication failure. For each high-priority failure mode, targeted mitigation strategies were proposed, encompassing enhanced condition monitoring, retrofitting of wear-resistant materials, and redesign of high-stress interfaces. Furthermore, failure detection mechanisms were improved through the integration of predictive maintenance protocols and sensor-based diagnostics. Implementation of these recommendations has resulted in measurable reductions in unplanned downtime, repair frequency, and maintenance overhead. This investigation demonstrates that FMECA, though underutilized in conventional foundry environments, offers a structured, data-driven methodology for uncovering latent failure risks and implementing preventive measures in complex industrial systems. By embedding FMECA within routine maintenance frameworks, a substantial improvement in operational resilience and equipment lifespan can be achieved. The findings support the strategic integration of reliability engineering methodologies into sand molding operations, contributing not only to cost efficiency but also to the broader adoption of systematic risk management practices in process-driven manufacturing sectors. 07-15-2025 <![CDATA[ In modern foundry operations, the reliability and operational continuity of sand molding systems are pivotal to maintaining productivity, safety, and competitive advantage. In this study, Failure Mode, Effects, and Criticality Analysis (FMECA) has been employed to systematically evaluate and optimize the performance of a pneumatic molding cell utilized in the production of sand molds. Particular focus has been directed toward the pusher subsystem, which is frequently subjected to high mechanical loads and cyclic stress, rendering it susceptible to recurrent failures that compromise both uptime and process efficiency. Potential failure modes were exhaustively identified, categorized, and prioritized based on their severity, occurrence, and detectability. Critical components, including servo motors, pneumatic actuators, and gearbox assemblies, were found to pose substantial risk to system reliability due to wear-induced degradation, misalignment, and lubrication failure. For each high-priority failure mode, targeted mitigation strategies were proposed, encompassing enhanced condition monitoring, retrofitting of wear-resistant materials, and redesign of high-stress interfaces. Furthermore, failure detection mechanisms were improved through the integration of predictive maintenance protocols and sensor-based diagnostics. Implementation of these recommendations has resulted in measurable reductions in unplanned downtime, repair frequency, and maintenance overhead. This investigation demonstrates that FMECA, though underutilized in conventional foundry environments, offers a structured, data-driven methodology for uncovering latent failure risks and implementing preventive measures in complex industrial systems. By embedding FMECA within routine maintenance frameworks, a substantial improvement in operational resilience and equipment lifespan can be achieved. The findings support the strategic integration of reliability engineering methodologies into sand molding operations, contributing not only to cost efficiency but also to the broader adoption of systematic risk management practices in process-driven manufacturing sectors. ]]> Criticality-Driven Reliability Enhancement of Pneumatic Sand Molding Cells in Foundry Applications via FMECA annunziata fiorilli vincenzo pezzotta cristiano fragassa doi: 10.56578/jemse040303 Journal of Engineering Management and Systems Engineering 07-15-2025 Journal of Engineering Management and Systems Engineering 07-15-2025 2025 4 3 Article 188 10.56578/jemse040303 https://www.acadlore.com/article/JEMSE/2025_4_3/jemse040303 https://www.acadlore.com/article/JEMSE/2025_4_3/jemse040302 A comprehensive statistical analysis was conducted to investigate the causes and prioritization of failure modes within a production line manufacturing leather covers for automotive interiors. The study was grounded in a Process Failure Mode and Effects Analysis (PFMEA), with a dual emphasis on evaluating the traditional Risk Priority Number (RPN) approach and the more contemporary Action Priority (AP) methodology, which has been increasingly adopted to enhance risk assessment sensitivity. Failure modes were classified and prioritized using both approaches, revealing notable differences in the ranking outcomes. To further elucidate the underlying contributors to these failure modes, causal factors were systematically categorized in accordance with the 5M+1E framework—Man, Machine, Method, Material, Measurement, and Environment—commonly employed in quality and reliability engineering. A cause-and-effect diagram was constructed to visualize the distribution of root causes across these categories. Descriptive statistics and correlation analyses were employed to quantify the relationship between each category and the prioritized failure modes. Particular attention was paid to examining the interdependencies among the core PFMEA parameters—Severity, Occurrence, and Detection—in order to determine their respective contributions to the variability in failure mode rankings. It was found that Severity exerted the most substantial influence on the prioritization outcomes under the AP model, while Occurrence was more dominant when the RPN method was applied. These findings suggest that the choice of prioritization method significantly alters the interpretation of risk and resource allocation for corrective actions. The integration of 5M+1E categorization with PFMEA metrics offers a structured pathway to enhance the diagnostic capability of reliability assessments and improve decision-making in failure prevention strategies. This approach is proposed as a more robust alternative to traditional analysis, enabling more precise targeting of corrective and preventive measures in high-precision manufacturing environments. 07-07-2025 <![CDATA[ A comprehensive statistical analysis was conducted to investigate the causes and prioritization of failure modes within a production line manufacturing leather covers for automotive interiors. The study was grounded in a Process Failure Mode and Effects Analysis (PFMEA), with a dual emphasis on evaluating the traditional Risk Priority Number (RPN) approach and the more contemporary Action Priority (AP) methodology, which has been increasingly adopted to enhance risk assessment sensitivity. Failure modes were classified and prioritized using both approaches, revealing notable differences in the ranking outcomes. To further elucidate the underlying contributors to these failure modes, causal factors were systematically categorized in accordance with the 5M+1E framework—Man, Machine, Method, Material, Measurement, and Environment—commonly employed in quality and reliability engineering. A cause-and-effect diagram was constructed to visualize the distribution of root causes across these categories. Descriptive statistics and correlation analyses were employed to quantify the relationship between each category and the prioritized failure modes. Particular attention was paid to examining the interdependencies among the core PFMEA parameters—Severity, Occurrence, and Detection—in order to determine their respective contributions to the variability in failure mode rankings. It was found that Severity exerted the most substantial influence on the prioritization outcomes under the AP model, while Occurrence was more dominant when the RPN method was applied. These findings suggest that the choice of prioritization method significantly alters the interpretation of risk and resource allocation for corrective actions. The integration of 5M+1E categorization with PFMEA metrics offers a structured pathway to enhance the diagnostic capability of reliability assessments and improve decision-making in failure prevention strategies. This approach is proposed as a more robust alternative to traditional analysis, enabling more precise targeting of corrective and preventive measures in high-precision manufacturing environments. ]]> Statistical Evaluation of Failure Mode Drivers via the 5M+1E Framework for Enhanced Reliability Control in Automotive Interior Manufacturing nikola banduka amanda aljinović meštrović doi: 10.56578/jemse040302 Journal of Engineering Management and Systems Engineering 07-07-2025 Journal of Engineering Management and Systems Engineering 07-07-2025 2025 4 3 Article 176 10.56578/jemse040302 https://www.acadlore.com/article/JEMSE/2025_4_3/jemse040302 https://www.acadlore.com/article/JEMSE/2025_4_3/jemse040301 Wireless communication technology has transformed connectivity across industries, but its widespread adoption comes with significant challenges. The purpose of paper is to identify and analyze the most critical obstacles affecting the efficiency, reliability, and scalability of wireless communication systems. This research paper mainly demonstrates to determine the most effective challenges for wireless communication technology. In recent times, it is really very significant and demanding work of this technology-based society. Interference, security vulnerabilities, bandwidth limitations, signal attenuation, and latency concerns etc. are the basic factors of this challenging work. This study explores the application of multi-criteria decision making (MCDM) techniques using intuitionistic fuzzy numbers (IFNs) to evaluate this. We apply the weighted MCDM method, i.e., Entropy in this paper. The decisions of multiple decision makers (DMs) are considered into account when collecting this problem related data and IFNs are utilised as mathematical tools to handle uncertainty. In order to address the ambiguity and inconsistency of the system, we finally conclude to conduct the analysis here with final result. 06-15-2025 <![CDATA[ Wireless communication technology has transformed connectivity across industries, but its widespread adoption comes with significant challenges. The purpose of paper is to identify and analyze the most critical obstacles affecting the efficiency, reliability, and scalability of wireless communication systems. This research paper mainly demonstrates to determine the most effective challenges for wireless communication technology. In recent times, it is really very significant and demanding work of this technology-based society. Interference, security vulnerabilities, bandwidth limitations, signal attenuation, and latency concerns etc. are the basic factors of this challenging work. This study explores the application of multi-criteria decision making (MCDM) techniques using intuitionistic fuzzy numbers (IFNs) to evaluate this. We apply the weighted MCDM method, i.e., Entropy in this paper. The decisions of multiple decision makers (DMs) are considered into account when collecting this problem related data and IFNs are utilised as mathematical tools to handle uncertainty. In order to address the ambiguity and inconsistency of the system, we finally conclude to conduct the analysis here with final result. ]]> Strategic Anagement of Wimreless Communication Challenges: Data-Driven Analysis for Enhanced Efficiency and Scalability in Uncertain Environment aditi biswas kamal hossain gazi arijit ghosh sankar prasad mondal doi: 10.56578/jemse040301 Journal of Engineering Management and Systems Engineering 06-15-2025 Journal of Engineering Management and Systems Engineering 06-15-2025 2025 4 3 Article 161 10.56578/jemse040301 https://www.acadlore.com/article/JEMSE/2025_4_3/jemse040301 https://www.acadlore.com/article/JEMSE/2025_4_2/jemse040205 Underwater electroacoustic transducers detect and localize targets beneath the water surface by generating acoustic waves. Due to their high power and simple structure, Tonpilz transducers are commonly used in underwater applications. To enhance data transmission speed and improve target detection capabilities using these transducers, it is necessary to increase their frequency bandwidth. One method of broadening the bandwidth is by adding damping elements to the transducer; however, this approach reduces the transmitted voltage response. In other words, increasing the frequency bandwidth comes at the cost of a reduced voltage output. To address this issue, arrays are typically used. Arrays are groups of transducers arranged together to improve performance and direct acoustic energy in a desired direction. Since accurate identification and estimation of bandwidth are critical to the performance and efficiency of a transducer—and ultimately the electroacoustic array—and given the high cost of manufacturing such transducers and arrays, the finite element method (FEM) is considered a highly desirable tool for analyzing and estimating the frequency bandwidth of electroacoustic arrays. Planar arrays are the simplest type of array. In the present study, the frequency responses of several planar arrays in square, circular, and diamond configurations have been comprehensively examined using finite element modeling. The effects of changes in array geometry, as well as variations in the number of transducers and their spacing, on the arrays’ performance have been predicted. Based on the obtained results, among three kinds of square arrays with different inter-element spacing, the array with a spacing of 0.4$\lambda$ between transducers exhibits the widest bandwidth. Additionally, among the two simulated circular arrays, the one with more elements demonstrates a higher transmitted voltage response and broader bandwidth. Furthermore, altering the array shape can reduce side lobes and help achieve the desired beam pattern. Overall, selecting the optimal array depends on the intended application, operating range, working environment, existing noise levels, and potential interference sources. Depending on these conditions, any of the examined arrays can be utilized effectively. 06-08-2025 <![CDATA[ Underwater electroacoustic transducers detect and localize targets beneath the water surface by generating acoustic waves. Due to their high power and simple structure, Tonpilz transducers are commonly used in underwater applications. To enhance data transmission speed and improve target detection capabilities using these transducers, it is necessary to increase their frequency bandwidth. One method of broadening the bandwidth is by adding damping elements to the transducer; however, this approach reduces the transmitted voltage response. In other words, increasing the frequency bandwidth comes at the cost of a reduced voltage output. To address this issue, arrays are typically used. Arrays are groups of transducers arranged together to improve performance and direct acoustic energy in a desired direction. Since accurate identification and estimation of bandwidth are critical to the performance and efficiency of a transducer—and ultimately the electroacoustic array—and given the high cost of manufacturing such transducers and arrays, the finite element method (FEM) is considered a highly desirable tool for analyzing and estimating the frequency bandwidth of electroacoustic arrays. Planar arrays are the simplest type of array. In the present study, the frequency responses of several planar arrays in square, circular, and diamond configurations have been comprehensively examined using finite element modeling. The effects of changes in array geometry, as well as variations in the number of transducers and their spacing, on the arrays’ performance have been predicted. Based on the obtained results, among three kinds of square arrays with different inter-element spacing, the array with a spacing of 0.4$\lambda$ between transducers exhibits the widest bandwidth. Additionally, among the two simulated circular arrays, the one with more elements demonstrates a higher transmitted voltage response and broader bandwidth. Furthermore, altering the array shape can reduce side lobes and help achieve the desired beam pattern. Overall, selecting the optimal array depends on the intended application, operating range, working environment, existing noise levels, and potential interference sources. Depending on these conditions, any of the examined arrays can be utilized effectively. ]]> Identification and Estimation of the Bandwidth of Different Acoustic Arrays Composed of Tonpilz Transducers in Engineering Sonar Systems naghmeh sheida hamid m. sedighi ali valipour doi: 10.56578/jemse040205 Journal of Engineering Management and Systems Engineering 06-08-2025 Journal of Engineering Management and Systems Engineering 06-08-2025 2025 4 2 Article 133 10.56578/jemse040205 https://www.acadlore.com/article/JEMSE/2025_4_2/jemse040205 https://www.acadlore.com/article/JEMSE/2025_4_2/jemse040204 Selection of the optimal warehouse location represents a key strategic decision in modern logistics, particularly in the context of the rapid development of e-commerce and the increasing complexity of supply chains. The aim of this research is to identify the most favorable warehouse location within the urban area of Belgrade by applying multi-criteria decision-making (MCDM) methods. Specifically, a hybrid methodology that integrates the Step-wise Weight Assessment Ratio Analysis (SWARA) and Additive Ratio Assessment (ARAS) methods was employed to evaluate five real-world alternative locations based on eight relevant criteria. The considered criteria include: land cost, delivery time, infrastructure accessibility, labor availability, access to multiple modes of transport, site capacity, environmental conditions and regulatory compliance, as well as the competitiveness of the location itself. Criterion weights were determined through expert evaluation using the SWARA method, while the ARAS method was applied to rank the alternatives based on their normalized performance scores. The analysis indicated that the location in Batajnica (A1) is the most favorable, closely followed by the location on Pančevački Road (A3), owing to their balanced performance across economic, infrastructural, and operational dimensions. In contrast, the location in Kaluđerica/Leštane (A4) proved to be the least suitable, primarily due to poor infrastructure access and limited labor availability. The results confirm the applicability and effectiveness of combining SWARA and ARAS methods for solving complex decision-making problems involving multiple, often conflicting, criteria. 05-22-2025 <![CDATA[ Selection of the optimal warehouse location represents a key strategic decision in modern logistics, particularly in the context of the rapid development of e-commerce and the increasing complexity of supply chains. The aim of this research is to identify the most favorable warehouse location within the urban area of Belgrade by applying multi-criteria decision-making (MCDM) methods. Specifically, a hybrid methodology that integrates the Step-wise Weight Assessment Ratio Analysis (SWARA) and Additive Ratio Assessment (ARAS) methods was employed to evaluate five real-world alternative locations based on eight relevant criteria. The considered criteria include: land cost, delivery time, infrastructure accessibility, labor availability, access to multiple modes of transport, site capacity, environmental conditions and regulatory compliance, as well as the competitiveness of the location itself. Criterion weights were determined through expert evaluation using the SWARA method, while the ARAS method was applied to rank the alternatives based on their normalized performance scores. The analysis indicated that the location in Batajnica (A1) is the most favorable, closely followed by the location on Pančevački Road (A3), owing to their balanced performance across economic, infrastructural, and operational dimensions. In contrast, the location in Kaluđerica/Leštane (A4) proved to be the least suitable, primarily due to poor infrastructure access and limited labor availability. The results confirm the applicability and effectiveness of combining SWARA and ARAS methods for solving complex decision-making problems involving multiple, often conflicting, criteria. ]]> A Multi-Criteria Analysis for E-commerce Warehouse Location Selection Using SWARA and ARAS Methods janja kozoderović vukašin pajić milan andrejić doi: 10.56578/jemse040204 Journal of Engineering Management and Systems Engineering 05-22-2025 Journal of Engineering Management and Systems Engineering 05-22-2025 2025 4 2 Article 122 10.56578/jemse040204 https://www.acadlore.com/article/JEMSE/2025_4_2/jemse040204 https://www.acadlore.com/article/JEMSE/2025_4_2/jemse040203 Energy remains a cornerstone of national economic development and societal advancement. However, the current trajectory of global energy production—dominated by fossil fuels and driven by escalating demand—is environmentally unsustainable. Electricity, as a versatile and high-grade form of energy, offers the advantage of being generable from both conventional and renewable sources. Nevertheless, fossil fuel–based electricity generation continues to contribute significantly to local and global environmental degradation. In response to the dual imperatives of meeting rising energy demand and reducing greenhouse gas emissions, the identification and prioritisation of sustainable electricity generation technologies have become imperative. Renewable energy sources (RES)—such as solar, wind, hydro, and biogas—offer viable alternatives, yet their relative merits must be evaluated through a rigorous and systematic approach. In this study, a multi-criteria decision-making (MCDM) framework has been employed to assess and rank RES in the Republic of Serbia. Key evaluation criteria have included construction cost, payback period, ecological impact, annual generation capacity, and potential for integration with alternative energy modes. The assessment has been conducted using the FANMA method (a novel hybrid technique named after its developers) and the Weighted Aggregated Sum Product Assessment (WASPAS) method, both of which are established tools for handling complex decision-making scenarios. The findings have provided a data-driven basis for prioritising renewable energy technologies in national energy strategies. The insights derived are expected to inform policy decisions in Serbia and offer a transferable framework for energy planning in other developing economies aiming to transition towards more sustainable power generation systems. 05-14-2025 <![CDATA[ Energy remains a cornerstone of national economic development and societal advancement. However, the current trajectory of global energy production—dominated by fossil fuels and driven by escalating demand—is environmentally unsustainable. Electricity, as a versatile and high-grade form of energy, offers the advantage of being generable from both conventional and renewable sources. Nevertheless, fossil fuel–based electricity generation continues to contribute significantly to local and global environmental degradation. In response to the dual imperatives of meeting rising energy demand and reducing greenhouse gas emissions, the identification and prioritisation of sustainable electricity generation technologies have become imperative. Renewable energy sources (RES)—such as solar, wind, hydro, and biogas—offer viable alternatives, yet their relative merits must be evaluated through a rigorous and systematic approach. In this study, a multi-criteria decision-making (MCDM) framework has been employed to assess and rank RES in the Republic of Serbia. Key evaluation criteria have included construction cost, payback period, ecological impact, annual generation capacity, and potential for integration with alternative energy modes. The assessment has been conducted using the FANMA method (a novel hybrid technique named after its developers) and the Weighted Aggregated Sum Product Assessment (WASPAS) method, both of which are established tools for handling complex decision-making scenarios. The findings have provided a data-driven basis for prioritising renewable energy technologies in national energy strategies. The insights derived are expected to inform policy decisions in Serbia and offer a transferable framework for energy planning in other developing economies aiming to transition towards more sustainable power generation systems. ]]> Multi-Criteria Decision-Making for Ranking Renewable Energy Sources: A Case Study from the Republic of Serbia nabankur mandal pallab sarkar nikola petrović doi: 10.56578/jemse040203 Journal of Engineering Management and Systems Engineering 05-14-2025 Journal of Engineering Management and Systems Engineering 05-14-2025 2025 4 2 Article 109 10.56578/jemse040203 https://www.acadlore.com/article/JEMSE/2025_4_2/jemse040203 https://www.acadlore.com/article/JEMSE/2025_4_2/jemse040202 The increasing demand for efficient and sustainable last-mile delivery solutions has presented a significant challenge in the evolving landscape of e-commerce logistics. To address this issue, a systematic evaluation and prioritization of six alternative delivery methods—namely, home delivery, workplace delivery, delivery to a neighbor or acquaintance, staffed pickup points, unstaffed (automated) pickup points, and third-party drop-off locations—has been conducted. These alternatives have been assessed against a comprehensive set of criteria, including delivery time flexibility, accessibility, cost-efficiency, security, speed of service, and ease of product return. To capture the nuanced preferences and subjective judgements of stakeholders, the Fuzzy Factor Relationship (FARE) method has been employed to determine the relative importance of each criterion through a structured fuzzy logic framework. Subsequently, the Aggregated Decision-Making (ADAM) method has been applied to rank the delivery alternatives, integrating evaluations from key stakeholder groups—consumers, retailers, and logistics service providers. The findings reveal that unstaffed pickup points, particularly those leveraging automated systems, represent the most balanced and sustainable solution, offering superior performance in terms of cost-effectiveness, user accessibility, and operational flexibility. In contrast, while home delivery continues to be favored for its convenience, it remains constrained by elevated operational costs and limited scheduling flexibility. The methodological integration of Fuzzy FARE and ADAM ensures a robust and transparent decision-support mechanism that accounts for both qualitative and quantitative factors. These insights are expected to guide strategic decision-making in last-mile logistics (LML), contributing to service quality enhancement, operational cost reduction, and the advancement of environmentally responsible delivery systems. This evaluation framework offers practical relevance to e-commerce platforms, third-party logistics providers, and urban mobility planners seeking to implement scalable and customer-centric delivery models in complex urban environments. 04-28-2025 <![CDATA[ The increasing demand for efficient and sustainable last-mile delivery solutions has presented a significant challenge in the evolving landscape of e-commerce logistics. To address this issue, a systematic evaluation and prioritization of six alternative delivery methods—namely, home delivery, workplace delivery, delivery to a neighbor or acquaintance, staffed pickup points, unstaffed (automated) pickup points, and third-party drop-off locations—has been conducted. These alternatives have been assessed against a comprehensive set of criteria, including delivery time flexibility, accessibility, cost-efficiency, security, speed of service, and ease of product return. To capture the nuanced preferences and subjective judgements of stakeholders, the Fuzzy Factor Relationship (FARE) method has been employed to determine the relative importance of each criterion through a structured fuzzy logic framework. Subsequently, the Aggregated Decision-Making (ADAM) method has been applied to rank the delivery alternatives, integrating evaluations from key stakeholder groups—consumers, retailers, and logistics service providers. The findings reveal that unstaffed pickup points, particularly those leveraging automated systems, represent the most balanced and sustainable solution, offering superior performance in terms of cost-effectiveness, user accessibility, and operational flexibility. In contrast, while home delivery continues to be favored for its convenience, it remains constrained by elevated operational costs and limited scheduling flexibility. The methodological integration of Fuzzy FARE and ADAM ensures a robust and transparent decision-support mechanism that accounts for both qualitative and quantitative factors. These insights are expected to guide strategic decision-making in last-mile logistics (LML), contributing to service quality enhancement, operational cost reduction, and the advancement of environmentally responsible delivery systems. This evaluation framework offers practical relevance to e-commerce platforms, third-party logistics providers, and urban mobility planners seeking to implement scalable and customer-centric delivery models in complex urban environments. ]]> Optimization of Last-Mile Delivery Alternatives Using the Fuzzy FARE and ADAM Multi-Criteria Decision-Making Methods mladen krstić snežana tadić anđela čvorović miloš veljović doi: 10.56578/jemse040202 Journal of Engineering Management and Systems Engineering 04-28-2025 Journal of Engineering Management and Systems Engineering 04-28-2025 2025 4 2 Article 98 10.56578/jemse040202 https://www.acadlore.com/article/JEMSE/2025_4_2/jemse040202 https://www.acadlore.com/article/JEMSE/2025_4_2/jemse040201 To mitigate safety risks in subway shield construction within water-rich silty fine sand layers, a risk immunization strategy based on complex network theory was proposed. Safety risk factors were systematically identified through literature review and expert consultation, and their relationships were modeled as a complex network. Unlike traditional single-index analyses, this study integrated degree centrality, betweenness centrality, eigenvector centrality, and clustering coefficient centrality to comprehensively evaluate the importance of risk factors. Results indicated that targeted immunization strategies significantly outperformed random immunization, with degree centrality (DC) and betweenness centrality (BC) immunization demonstrating the best performance. Key risk sources included stratum stability, allowable surface deformation, surface settlement monitoring, and shield tunneling control. Furthermore, the optimal two-factor coupling immunization strategy was found to be the combination of DC and BC strategies, which provided the most effective risk prevention. This study is the first to apply complex network immunization simulation to safety risk management in subway shield construction, enhancing the risk index system and validating the impact of different immunization strategies on overall safety. The findings offer scientific guidance for risk management in complex geological conditions and provide theoretical support and practical insights for improving construction safety. 04-02-2025 <![CDATA[ To mitigate safety risks in subway shield construction within water-rich silty fine sand layers, a risk immunization strategy based on complex network theory was proposed. Safety risk factors were systematically identified through literature review and expert consultation, and their relationships were modeled as a complex network. Unlike traditional single-index analyses, this study integrated degree centrality, betweenness centrality, eigenvector centrality, and clustering coefficient centrality to comprehensively evaluate the importance of risk factors. Results indicated that targeted immunization strategies significantly outperformed random immunization, with degree centrality (DC) and betweenness centrality (BC) immunization demonstrating the best performance. Key risk sources included stratum stability, allowable surface deformation, surface settlement monitoring, and shield tunneling control. Furthermore, the optimal two-factor coupling immunization strategy was found to be the combination of DC and BC strategies, which provided the most effective risk prevention. This study is the first to apply complex network immunization simulation to safety risk management in subway shield construction, enhancing the risk index system and validating the impact of different immunization strategies on overall safety. The findings offer scientific guidance for risk management in complex geological conditions and provide theoretical support and practical insights for improving construction safety. ]]> Safety Risk Immunization Strategies for Subway Shield Construction in Water-Rich Silty Fine Sand Layers: A Complex Network Approach linyu li doi: 10.56578/jemse040201 Journal of Engineering Management and Systems Engineering 04-02-2025 Journal of Engineering Management and Systems Engineering 04-02-2025 2025 4 2 Article 83 10.56578/jemse040201 https://www.acadlore.com/article/JEMSE/2025_4_2/jemse040201 https://www.acadlore.com/article/JEMSE/2025_4_1/jemse040105 In recent years, frequent natural disasters and public emergencies have emphasized the importance of emergency material distribution path planning. Aiming at the problems of neglecting the differences in the urgency of the demand at the disaster-stricken points and the lack of distribution fairness in traditional research, this study proposes an emergency material distribution path planning method that integrates the priority assessment of the disaster-stricken points and multi-objective optimization. First of all, a two-level evaluation system is constructed from the dimensions of disaster degree and material demand, including the number of rescue population and other indicators, and the combined weights are calculated by combining the subjective and objective methods of hierarchical analysis (AHP) and entropy weighting, so as to quantify the urgency coefficient of the demand at each disaster site and break through the limitations of the traditional “nearby distribution” mode. On this basis, a vehicle path planning model is established with the dual objectives of minimizing the total distribution cost and vehicle load balance, and the elite strategy non-dominated sorting genetic algorithm (NSGA-II) is introduced to solve the problem. Scenario analysis is carried out with the background of public health emergencies in Jingzhou City, and the effectiveness of the model is verified based on the actual data of 64 medical material demand points. The simulation results show that the total distribution distance and vehicle load balance are optimized after optimization. Finally, it is suggested in conjunction with the current situation of emergency material distribution in China. Through the quantification of demand urgency and multi-objective collaborative optimization, this study provides theoretical basis and practical reference for improving the fairness, timeliness and resource utilization efficiency of emergency logistics, and has important application value for improving disaster relief decision-making. 03-20-2025 <![CDATA[ <p>In recent years, frequent natural disasters and public emergencies have emphasized the importance of emergency material distribution path planning. Aiming at the problems of neglecting the differences in the urgency of the demand at the disaster-stricken points and the lack of distribution fairness in traditional research, this study proposes an emergency material distribution path planning method that integrates the priority assessment of the disaster-stricken points and multi-objective optimization. First of all, a two-level evaluation system is constructed from the dimensions of disaster degree and material demand, including the number of rescue population and other indicators, and the combined weights are calculated by combining the subjective and objective methods of hierarchical analysis (AHP) and entropy weighting, so as to quantify the urgency coefficient of the demand at each disaster site and break through the limitations of the traditional “nearby distribution” mode. On this basis, a vehicle path planning model is established with the dual objectives of minimizing the total distribution cost and vehicle load balance, and the elite strategy non-dominated sorting genetic algorithm (NSGA-II) is introduced to solve the problem. Scenario analysis is carried out with the background of public health emergencies in Jingzhou City, and the effectiveness of the model is verified based on the actual data of 64 medical material demand points. The simulation results show that the total distribution distance and vehicle load balance are optimized after optimization. Finally, it is suggested in conjunction with the current situation of emergency material distribution in China. Through the quantification of demand urgency and multi-objective collaborative optimization, this study provides theoretical basis and practical reference for improving the fairness, timeliness and resource utilization efficiency of emergency logistics, and has important application value for improving disaster relief decision-making.</p> ]]> Strategic Distribution of Emergency Resources: A Multi-Objective Approach with NSGA-II and Prioritization of Affected Areas jing gao mengting yao zhuang wu xiaoyan deng xuemei yu lina yu doi: 10.56578/jemse040105 Journal of Engineering Management and Systems Engineering 03-20-2025 Journal of Engineering Management and Systems Engineering 03-20-2025 2025 4 1 Article 67 10.56578/jemse040105 https://www.acadlore.com/article/JEMSE/2025_4_1/jemse040105 https://www.acadlore.com/article/JEMSE/2025_4_1/jemse040104 The Location-Routing Problem (LRP) involves the simultaneous determination of optimal facility locations and vehicle routing strategies to fulfill customer demands while adhering to operational constraints. Traditional formulations of the LRP primarily focus on delivery-only scenarios, where goods are allocated from designated warehouses to customers through a fleet of vehicles. However, real-world logistics often necessitate the simultaneous handling of both deliveries and pickups, introducing additional complexity. Furthermore, inherent uncertainties in demand patterns make precise parameter estimation challenging, particularly regarding the quantities of goods received and dispatched by customers. To enhance the realism of the model, these demand variables are represented using fuzzy sets, capturing the uncertainty inherent in practical logistics operations. A mathematical model is developed to account for these complexities, incorporating a heterogeneous fleet of vehicles with capacity constraints. The optimization of the proposed fuzzy capacitated LRP with simultaneous pickup and delivery is conducted using a Genetic Algorithm (GA) tailored for fuzzy environments. The efficacy of the proposed approach is validated through numerical experiments, demonstrating its capability to generate high-quality solutions under uncertain conditions. The findings contribute to the advancement of location-routing optimization methodologies, providing a robust framework for decision-making in uncertain logistics environments. 03-19-2025 <![CDATA[ The Location-Routing Problem (LRP) involves the simultaneous determination of optimal facility locations and vehicle routing strategies to fulfill customer demands while adhering to operational constraints. Traditional formulations of the LRP primarily focus on delivery-only scenarios, where goods are allocated from designated warehouses to customers through a fleet of vehicles. However, real-world logistics often necessitate the simultaneous handling of both deliveries and pickups, introducing additional complexity. Furthermore, inherent uncertainties in demand patterns make precise parameter estimation challenging, particularly regarding the quantities of goods received and dispatched by customers. To enhance the realism of the model, these demand variables are represented using fuzzy sets, capturing the uncertainty inherent in practical logistics operations. A mathematical model is developed to account for these complexities, incorporating a heterogeneous fleet of vehicles with capacity constraints. The optimization of the proposed fuzzy capacitated LRP with simultaneous pickup and delivery is conducted using a Genetic Algorithm (GA) tailored for fuzzy environments. The efficacy of the proposed approach is validated through numerical experiments, demonstrating its capability to generate high-quality solutions under uncertain conditions. The findings contribute to the advancement of location-routing optimization methodologies, providing a robust framework for decision-making in uncertain logistics environments. ]]> Genetic Algorithm-Based Optimization for the Fuzzy Capacitated Location-Routing Problem with Simultaneous Pickup and Delivery hamed fazlollahtabar doi: 10.56578/jemse040104 Journal of Engineering Management and Systems Engineering 03-19-2025 Journal of Engineering Management and Systems Engineering 03-19-2025 2025 4 1 Article 50 10.56578/jemse040104 https://www.acadlore.com/article/JEMSE/2025_4_1/jemse040104 https://www.acadlore.com/article/JEMSE/2025_4_1/jemse040103 This study investigates the application of numerical simulations to optimize the design and operational performance of CNC machining centers, with a focus on enhancing their structural integrity and durability. The primary objective is to identify design modifications that can mitigate the risks associated with mechanical impacts and extend the service life of the machines. Finite Element Method (FEM) simulations are conducted on actual CNC machines to examine their structural responses under a range of real-world impact scenarios. The simulations reveal critical stress concentrations and deformation patterns that occur in operational environments, providing valuable insights into the dynamic behavior of the machines. A system engineering approach is employed to simplify the analysis of the machine's response to these dynamic conditions, allowing for an efficient evaluation of potential design improvements. Linear static analyses, incorporating imposed deformation conditions, are used to gain a deeper understanding of the machine’s structural weaknesses. Several model simplifications are introduced, including modifications to geometry, contact conditions, and material properties, ensuring that the quality and accuracy of the numerical models are maintained. The results highlight the potential for targeted design modifications to reduce the likelihood of mechanical failure and enhance operational efficiency. These findings suggest that the application of advanced computational mechanics can substantially improve machine performance, ultimately contributing to the longevity and reliability of CNC machining centers. 03-13-2025 <![CDATA[ This study investigates the application of numerical simulations to optimize the design and operational performance of CNC machining centers, with a focus on enhancing their structural integrity and durability. The primary objective is to identify design modifications that can mitigate the risks associated with mechanical impacts and extend the service life of the machines. Finite Element Method (FEM) simulations are conducted on actual CNC machines to examine their structural responses under a range of real-world impact scenarios. The simulations reveal critical stress concentrations and deformation patterns that occur in operational environments, providing valuable insights into the dynamic behavior of the machines. A system engineering approach is employed to simplify the analysis of the machine's response to these dynamic conditions, allowing for an efficient evaluation of potential design improvements. Linear static analyses, incorporating imposed deformation conditions, are used to gain a deeper understanding of the machine’s structural weaknesses. Several model simplifications are introduced, including modifications to geometry, contact conditions, and material properties, ensuring that the quality and accuracy of the numerical models are maintained. The results highlight the potential for targeted design modifications to reduce the likelihood of mechanical failure and enhance operational efficiency. These findings suggest that the application of advanced computational mechanics can substantially improve machine performance, ultimately contributing to the longevity and reliability of CNC machining centers. ]]> System Engineering Approaches for Enhancing the Structural Integrity and Operational Efficiency of CNC Machining Centers: A Numerical Simulation Study simone pazzagli ana pavlovic dragan marinkovic cristiano fragassa doi: 10.56578/jemse040103 Journal of Engineering Management and Systems Engineering 03-13-2025 Journal of Engineering Management and Systems Engineering 03-13-2025 2025 4 1 Article 39 10.56578/jemse040103 https://www.acadlore.com/article/JEMSE/2025_4_1/jemse040103 https://www.acadlore.com/article/JEMSE/2025_4_1/jemse040102 In the context of today’s rapidly evolving automotive market, improving the reliability and efficiency of manufacturing processes remains a critical challenge for industry players. This study introduces a hybrid multi-attribute decision-making model that integrates Failure Mode and Effects Analysis (FMEA) with interval type-2 fuzzy set theory to classify and prioritize process failures. The approach enables the FMEA team to systematically identify and rank failure modes, facilitating the timely implementation of corrective actions aimed at enhancing process reliability. A key feature of the proposed model is the utilization of interval type-2 triangular fuzzy numbers (IT2TFNs), which capture the inherent uncertainty in expert assessments of risk factors (RFs). These fuzzy values are aggregated using the fuzzy harmonic mean, and the total relation matrix is derived by applying fuzzy algebraic operations, followed by defuzzification and distance calculations between fuzzy numbers. The modified Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is employed to determine the relative weights of identified RFs, while the Multi-Attributive Border Approximation Area Comparison (MABAC) technique is used to rank failure modes based on their impact on manufacturing process reliability. The model’s effectiveness is demonstrated through its application to real-world data from an automotive supply chain, highlighting its superior capability compared to conventional approaches. This research contributes to the advancement of failure management strategies, providing a comprehensive and robust framework for decision-making in complex manufacturing environments. 03-13-2025 <![CDATA[ In the context of today’s rapidly evolving automotive market, improving the reliability and efficiency of manufacturing processes remains a critical challenge for industry players. This study introduces a hybrid multi-attribute decision-making model that integrates Failure Mode and Effects Analysis (FMEA) with interval type-2 fuzzy set theory to classify and prioritize process failures. The approach enables the FMEA team to systematically identify and rank failure modes, facilitating the timely implementation of corrective actions aimed at enhancing process reliability. A key feature of the proposed model is the utilization of interval type-2 triangular fuzzy numbers (IT2TFNs), which capture the inherent uncertainty in expert assessments of risk factors (RFs). These fuzzy values are aggregated using the fuzzy harmonic mean, and the total relation matrix is derived by applying fuzzy algebraic operations, followed by defuzzification and distance calculations between fuzzy numbers. The modified Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is employed to determine the relative weights of identified RFs, while the Multi-Attributive Border Approximation Area Comparison (MABAC) technique is used to rank failure modes based on their impact on manufacturing process reliability. The model’s effectiveness is demonstrated through its application to real-world data from an automotive supply chain, highlighting its superior capability compared to conventional approaches. This research contributes to the advancement of failure management strategies, providing a comprehensive and robust framework for decision-making in complex manufacturing environments. ]]> A Hybrid Interval Type-2 Fuzzy DEMATEL-MABAC Approach for Strategic Failure Management in Automotive Manufacturing danijela tadić nikola komatina doi: 10.56578/jemse040102 Journal of Engineering Management and Systems Engineering 03-13-2025 Journal of Engineering Management and Systems Engineering 03-13-2025 2025 4 1 Article 21 10.56578/jemse040102 https://www.acadlore.com/article/JEMSE/2025_4_1/jemse040102 https://www.acadlore.com/article/JEMSE/2025_4_1/jemse040101 Multi-functional public teaching buildings, as high-density spaces, are subject to significant fire risks due to the large number of occupants and the complex nature of their design. In the event of a fire, the consequences can be catastrophic. Therefore, fire risk assessment is of paramount importance in the design and operation of such buildings. A comprehensive evaluation framework is proposed, integrating the Work Breakdown Structure (WBS) and the Risk Breakdown Structure (RBS) into a unified approach, referred to as the Integrated Work Breakdown Structure and Risk Breakdown Structure (i-WRBS) method. This framework identifies 15 key fire risk factors relevant to public school buildings. The Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is employed to analyze the interrelationships among these factors, while PyroSim fire simulation software is used to model the dynamics of fire smoke propagation under varying wind conditions. The diffusion of smoke in stairwells is simulated under different wind speeds and directions, and the fire risk is evaluated based on the resulting outcomes. The findings indicate that both wind speed and direction play a crucial role in determining the trajectory and velocity of smoke spread, especially within stairwells. Under low wind conditions or in the absence of wind, smoke diffusion is confined to areas close to the fire source, with stairwells located farther from the fire exhibiting comparatively lower risks. However, under higher wind speeds, the speed and range of smoke diffusion are significantly increased, with a pronounced effect in the downwind direction. The fire hazards on higher floors are found to be more sensitive to variations in wind speed, as increased wind velocity leads to more substantial fluctuations in temperature caused by the combustion process. These fluctuations are exacerbated on higher floors. The findings offer valuable insights into fire risk management, contributing to the development of fire safety strategies and the formulation of evacuation plans for large public buildings. 03-13-2025 <![CDATA[ Multi-functional public teaching buildings, as high-density spaces, are subject to significant fire risks due to the large number of occupants and the complex nature of their design. In the event of a fire, the consequences can be catastrophic. Therefore, fire risk assessment is of paramount importance in the design and operation of such buildings. A comprehensive evaluation framework is proposed, integrating the Work Breakdown Structure (WBS) and the Risk Breakdown Structure (RBS) into a unified approach, referred to as the Integrated Work Breakdown Structure and Risk Breakdown Structure (i-WRBS) method. This framework identifies 15 key fire risk factors relevant to public school buildings. The Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is employed to analyze the interrelationships among these factors, while PyroSim fire simulation software is used to model the dynamics of fire smoke propagation under varying wind conditions. The diffusion of smoke in stairwells is simulated under different wind speeds and directions, and the fire risk is evaluated based on the resulting outcomes. The findings indicate that both wind speed and direction play a crucial role in determining the trajectory and velocity of smoke spread, especially within stairwells. Under low wind conditions or in the absence of wind, smoke diffusion is confined to areas close to the fire source, with stairwells located farther from the fire exhibiting comparatively lower risks. However, under higher wind speeds, the speed and range of smoke diffusion are significantly increased, with a pronounced effect in the downwind direction. The fire hazards on higher floors are found to be more sensitive to variations in wind speed, as increased wind velocity leads to more substantial fluctuations in temperature caused by the combustion process. These fluctuations are exacerbated on higher floors. The findings offer valuable insights into fire risk management, contributing to the development of fire safety strategies and the formulation of evacuation plans for large public buildings. ]]> Fire Risk Assessment and Simulation of Multi-Functional Teaching Buildings: A Combined Application of DEMATEL and PyroSim hongjun he zaohong zhou yunbin sun qiang li doi: 10.56578/jemse040101 Journal of Engineering Management and Systems Engineering 03-13-2025 Journal of Engineering Management and Systems Engineering 03-13-2025 2025 4 1 Article 1 10.56578/jemse040101 https://www.acadlore.com/article/JEMSE/2025_4_1/jemse040101 https://www.acadlore.com/article/JEMSE/2024_3_4/jemse030405 Risk management in the transportation of dangerous goods is critical for safeguarding human health, the environment, and infrastructure. This study explores systematic methodologies for risk assessment in the context of hazardous materials transit, with a particular focus on the transport of bitumen in Hungary. Key techniques, including Failure Mode and Effect Analysis (FMEA), Hazard and Operability Analysis (HAZOP), and Bow-Tie Analysis, are employed to identify, evaluate, and prioritize risks associated with the transportation process. These approaches enable the systematic breakdown of potential failure points, the evaluation of their effects, and the identification of mitigation strategies. The case study on bitumen transport highlights several significant risk factors, including operational failures, human errors, and vehicle-related incidents. The analysis reveals the importance of robust safety measures, such as enhanced driver training, real-time monitoring systems, and comprehensive documentation protocols, in reducing the likelihood and impact of such incidents. Furthermore, the study advocates for the continuous improvement of risk assessment procedures, emphasizing the need for adaptation to evolving regulatory standards and emerging challenges in hazardous materials transport. The findings underscore the importance of a proactive safety culture that integrates both technical solutions and organizational practices, ensuring a comprehensive approach to risk management in the transport of dangerous goods (TDG). 12-17-2024 <![CDATA[ Risk management in the transportation of dangerous goods is critical for safeguarding human health, the environment, and infrastructure. This study explores systematic methodologies for risk assessment in the context of hazardous materials transit, with a particular focus on the transport of bitumen in Hungary. Key techniques, including Failure Mode and Effect Analysis (FMEA), Hazard and Operability Analysis (HAZOP), and Bow-Tie Analysis, are employed to identify, evaluate, and prioritize risks associated with the transportation process. These approaches enable the systematic breakdown of potential failure points, the evaluation of their effects, and the identification of mitigation strategies. The case study on bitumen transport highlights several significant risk factors, including operational failures, human errors, and vehicle-related incidents. The analysis reveals the importance of robust safety measures, such as enhanced driver training, real-time monitoring systems, and comprehensive documentation protocols, in reducing the likelihood and impact of such incidents. Furthermore, the study advocates for the continuous improvement of risk assessment procedures, emphasizing the need for adaptation to evolving regulatory standards and emerging challenges in hazardous materials transport. The findings underscore the importance of a proactive safety culture that integrates both technical solutions and organizational practices, ensuring a comprehensive approach to risk management in the transport of dangerous goods (TDG). ]]> Risk Management in the Transport of Dangerous Goods in Hungary: A Statistical and FMEA-Based Case Study on Bitumen Transportation ágota drégelyi-kiss georgina nóra tóth andrás horváth gabriella farkas doi: 10.56578/jemse030405 Journal of Engineering Management and Systems Engineering 12-17-2024 Journal of Engineering Management and Systems Engineering 12-17-2024 2024 3 4 Article 236 10.56578/jemse030405 https://www.acadlore.com/article/JEMSE/2024_3_4/jemse030405 https://www.acadlore.com/article/JEMSE/2024_3_4/jemse030404 The strategic location of emergency supply depots is critical for enhancing pre-disaster preparedness and post-disaster relief efforts. Given the inherent uncertainties and risks associated with natural and man-made disasters, ensuring the swift and effective delivery of relief materials to affected areas is pivotal for minimizing disaster impacts and safeguarding lives and property. This review synthesizes the current body of research on the siting of emergency stockpiles, providing a comprehensive analysis of the factors influencing site selection. Key factors such as the geographic scope of disaster response, hydrographic conditions, transportation infrastructure, and accessibility to affected populations are examined. Various siting models are evaluated to optimize resource allocation, minimize logistics costs, and improve supply chain responsiveness during emergencies. This review also identifies key challenges within the existing literature, including limitations in model algorithms, disaster stage considerations, optimization criteria, and the degree of stakeholder involvement in decision-making. Notably, while previous research has often focused on isolated factors, this study emphasizes the need for an integrated approach that accounts for dynamic, diversified, intelligent, and human-centered considerations. Dynamic models are essential to adapt to the unpredictable nature of disasters, while diversified approaches are necessary to address the varying needs of different disaster types and affected populations. Intelligent decision-making tools, incorporating data analytics and real-time information, can enhance the efficiency and accuracy of site selection processes. Human-centric models, focusing on the actual needs of disaster-affected communities, are critical for ensuring the effectiveness of relief operations. The review concludes by outlining future research directions, emphasizing the importance of developing adaptable, sustainable, and context-specific siting models. Future investigations should focus on the practical application of emerging technologies, such as big data analytics, artificial intelligence, and remote sensing, to refine siting models and improve their responsiveness in a rapidly changing global landscape. These advancements are expected to contribute to more efficient and cost-effective emergency supply systems, better equipped to address the evolving challenges of global disaster risks. 12-09-2024 <![CDATA[ The strategic location of emergency supply depots is critical for enhancing pre-disaster preparedness and post-disaster relief efforts. Given the inherent uncertainties and risks associated with natural and man-made disasters, ensuring the swift and effective delivery of relief materials to affected areas is pivotal for minimizing disaster impacts and safeguarding lives and property. This review synthesizes the current body of research on the siting of emergency stockpiles, providing a comprehensive analysis of the factors influencing site selection. Key factors such as the geographic scope of disaster response, hydrographic conditions, transportation infrastructure, and accessibility to affected populations are examined. Various siting models are evaluated to optimize resource allocation, minimize logistics costs, and improve supply chain responsiveness during emergencies. This review also identifies key challenges within the existing literature, including limitations in model algorithms, disaster stage considerations, optimization criteria, and the degree of stakeholder involvement in decision-making. Notably, while previous research has often focused on isolated factors, this study emphasizes the need for an integrated approach that accounts for dynamic, diversified, intelligent, and human-centered considerations. Dynamic models are essential to adapt to the unpredictable nature of disasters, while diversified approaches are necessary to address the varying needs of different disaster types and affected populations. Intelligent decision-making tools, incorporating data analytics and real-time information, can enhance the efficiency and accuracy of site selection processes. Human-centric models, focusing on the actual needs of disaster-affected communities, are critical for ensuring the effectiveness of relief operations. The review concludes by outlining future research directions, emphasizing the importance of developing adaptable, sustainable, and context-specific siting models. Future investigations should focus on the practical application of emerging technologies, such as big data analytics, artificial intelligence, and remote sensing, to refine siting models and improve their responsiveness in a rapidly changing global landscape. These advancements are expected to contribute to more efficient and cost-effective emergency supply systems, better equipped to address the evolving challenges of global disaster risks. ]]> Optimization of Emergency Stockpile Siting: A Review of Models, Influencing Factors, and Future Research Directions haiyong liu zhuang wu lina yu doi: 10.56578/jemse030404 Journal of Engineering Management and Systems Engineering 12-09-2024 Journal of Engineering Management and Systems Engineering 12-09-2024 2024 3 4 Article 226 10.56578/jemse030404 https://www.acadlore.com/article/JEMSE/2024_3_4/jemse030404 https://www.acadlore.com/article/JEMSE/2024_3_4/jemse030403 The shear connection behaviour of steel-concrete composite beams is primarily governed by the strength of the connectors and concrete. Modern seismic evaluations and vibrational analyses of composite beams, particularly concerning their load-slip characteristics and shear strength, predominantly rely on push-out test data. In this study, the Finite Element Method (FEM) has been employed to simulate and analyse the shear, bending, and deflection responses of composite beams subjected to various load conditions, in accordance with Eurocode 4 standards. Failure modes, ultimate loads, and sectional capacities were examined in detail. The results indicate that increased strength of both steel and concrete significantly enhances the beam’s capacity in bending. Specifically, flexural and compressive resistance showed marginal improvements of 3.2%, 3.1%, and 3.0%, respectively, as concrete strength increased from 25 N/mm² to 30, 35, and 40 N/mm², while steel strength increased by 27% and 21%, with yield strengths of 275 N/mm², 355 N/mm², and 460 N/mm², respectively. Under seismic loading, however, the ultimate flexural load capacity exhibited a reduction with a fixed beam span, irrespective of steel strength. The shear capacity remained constant across varying beam lengths but demonstrated significant improvements with increased steel yield strength, with enhancements of 29% and 67% as steel yield strength increased from 275 N/mm² to 355 N/mm² and 460 N/mm², respectively. A detailed vibration analysis was also conducted to investigate the dynamic behaviour of these composite beams under seismic conditions. These findings underscore the critical influence of material strengths and loading conditions on the performance of steel-concrete composite beams, particularly in seismic scenarios, providing valuable insights for the design and assessment of such structures in seismic-prone regions. 12-06-2024 <![CDATA[ <p>The shear connection behaviour of steel-concrete composite beams is primarily governed by the strength of the connectors and concrete. Modern seismic evaluations and vibrational analyses of composite beams, particularly concerning their load-slip characteristics and shear strength, predominantly rely on push-out test data. In this study, the Finite Element Method (FEM) has been employed to simulate and analyse the shear, bending, and deflection responses of composite beams subjected to various load conditions, in accordance with Eurocode 4 standards. Failure modes, ultimate loads, and sectional capacities were examined in detail. The results indicate that increased strength of both steel and concrete significantly enhances the beam’s capacity in bending. Specifically, flexural and compressive resistance showed marginal improvements of 3.2%, 3.1%, and 3.0%, respectively, as concrete strength increased from 25 N/mm² to 30, 35, and 40 N/mm², while steel strength increased by 27% and 21%, with yield strengths of 275 N/mm², 355 N/mm², and 460 N/mm², respectively. Under seismic loading, however, the ultimate flexural load capacity exhibited a reduction with a fixed beam span, irrespective of steel strength. The shear capacity remained constant across varying beam lengths but demonstrated significant improvements with increased steel yield strength, with enhancements of 29% and 67% as steel yield strength increased from 275 N/mm² to 355 N/mm² and 460 N/mm², respectively. A detailed vibration analysis was also conducted to investigate the dynamic behaviour of these composite beams under seismic conditions. These findings underscore the critical influence of material strengths and loading conditions on the performance of steel-concrete composite beams, particularly in seismic scenarios, providing valuable insights for the design and assessment of such structures in seismic-prone regions.</p> ]]> Shear Connection Behaviour and Performance of Steel-Concrete Composite Beams under Seismic and Load Conditions: A Finite Element Analysis isametova madina esdauletova yan cao miloš milovančević doi: 10.56578/jemse030403 Journal of Engineering Management and Systems Engineering 12-06-2024 Journal of Engineering Management and Systems Engineering 12-06-2024 2024 3 4 Article 210 10.56578/jemse030403 https://www.acadlore.com/article/JEMSE/2024_3_4/jemse030403 https://www.acadlore.com/article/JEMSE/2024_3_4/jemse030402 The challenge of providing students with practical, hands-on experience in realistic industrial environments is increasingly prevalent in modern technical education. The concept of a Learning Factory addresses this issue by facilitating skill acquisition through immersive, practice-oriented training that integrates advanced digital technologies. An innovative educational platform has been developed, incorporating Internet of Things (IoT) devices, Cyber-Physical Systems (CPS), and Digital Twin (DT) technology to enhance manufacturing education. This platform combines modular hardware and software, enabling immersive visualisation and real-time monitoring through DT-supported systems. These features offer a comprehensive, interactive learning experience that closely simulates real-world manufacturing processes. The system's smart reconfigurability further enhances its educational potential by enabling customisable training scenarios tailored to specific learning outcomes. The proposed approach aligns with the principles of Industry 4.0 and serves as a catalyst for the improvement of both educational and professional training environments. By leveraging digitalisation, this platform not only supports adaptive learning but also enhances the efficiency of educational models. Through the simulation of dynamic manufacturing systems, students are exposed to a variety of industrial scenarios, fostering deeper understanding and skill development. The integration of IoT, CPS, and DT technologies is expected to provide a scalable framework for future educational environments, ultimately improving the adaptability and effectiveness of manufacturing training. 12-02-2024 <![CDATA[ The challenge of providing students with practical, hands-on experience in realistic industrial environments is increasingly prevalent in modern technical education. The concept of a Learning Factory addresses this issue by facilitating skill acquisition through immersive, practice-oriented training that integrates advanced digital technologies. An innovative educational platform has been developed, incorporating Internet of Things (IoT) devices, Cyber-Physical Systems (CPS), and Digital Twin (DT) technology to enhance manufacturing education. This platform combines modular hardware and software, enabling immersive visualisation and real-time monitoring through DT-supported systems. These features offer a comprehensive, interactive learning experience that closely simulates real-world manufacturing processes. The system's smart reconfigurability further enhances its educational potential by enabling customisable training scenarios tailored to specific learning outcomes. The proposed approach aligns with the principles of Industry 4.0 and serves as a catalyst for the improvement of both educational and professional training environments. By leveraging digitalisation, this platform not only supports adaptive learning but also enhances the efficiency of educational models. Through the simulation of dynamic manufacturing systems, students are exposed to a variety of industrial scenarios, fostering deeper understanding and skill development. The integration of IoT, CPS, and DT technologies is expected to provide a scalable framework for future educational environments, ultimately improving the adaptability and effectiveness of manufacturing training. ]]> Development of an Immersive, Digital Twin-Supported Smart Reconfigurable Educational Platform for Manufacturing Training: A Proof of Concept norbert szántó gergő dávid monek szabolcs fischer doi: 10.56578/jemse030402 Journal of Engineering Management and Systems Engineering 12-02-2024 Journal of Engineering Management and Systems Engineering 12-02-2024 2024 3 4 Article 199 10.56578/jemse030402 https://www.acadlore.com/article/JEMSE/2024_3_4/jemse030402 https://www.acadlore.com/article/JEMSE/2024_3_4/jemse030401 Manufacturers are increasingly leveraging both online and offline channels to diversify their sales strategies. However, competition between these channels presents challenges in maximising profits for all parties involved. This study investigates the use of cost-sharing contracts by manufacturers to promote marketing in both online and offline channels, with the goal of achieving Pareto improvements in supply chain profitability. The model also accounts for consumers’ reference quality perceptions in online channels, offering a comprehensive evaluation of how cost-sharing contracts influence the operational strategies and performance of both online and offline enterprises. An empirical analysis is conducted using the “US Stores Sales” dataset from Kaggle, comprising 4,249 samples with 20 recorded characteristics per sample. The findings indicate that: (1) Cost-sharing in marketing efforts facilitates a Pareto improvement in profits for manufacturers, online enterprises, and offline retailers, with manufacturers experiencing the most significant benefit. (2) When the manufacturer assumes a larger share of marketing costs for one channel (e.g., online or offline) and a smaller share for the other, the party receiving the higher cost-sharing proportion typically sees increased profitability, while the other party’s profitability may diminish. (3) Empirical analysis suggests that manufacturers should prioritise supporting online businesses’ marketing activities, as this strategy is more likely to result in higher overall profits for the manufacturer. (4) Interestingly, when equal cost-sharing proportions are offered to both online and offline enterprises for the sake of fairness, the manufacturer’s profitability is enhanced. Moreover, the profitability of online enterprises tends to increase when the equal cost-sharing proportion is smaller. These findings validate the proposed model and underscore the critical role of strategic cost-sharing contracts in optimising Online to Offline (O2O) supply chain performance. Further research could explore the implications of varying consumer preferences and digitalisation trends on the effectiveness of such strategies. 09-23-2024 <![CDATA[ Manufacturers are increasingly leveraging both online and offline channels to diversify their sales strategies. However, competition between these channels presents challenges in maximising profits for all parties involved. This study investigates the use of cost-sharing contracts by manufacturers to promote marketing in both online and offline channels, with the goal of achieving Pareto improvements in supply chain profitability. The model also accounts for consumers’ reference quality perceptions in online channels, offering a comprehensive evaluation of how cost-sharing contracts influence the operational strategies and performance of both online and offline enterprises. An empirical analysis is conducted using the “US Stores Sales” dataset from Kaggle, comprising 4,249 samples with 20 recorded characteristics per sample. The findings indicate that: (1) Cost-sharing in marketing efforts facilitates a Pareto improvement in profits for manufacturers, online enterprises, and offline retailers, with manufacturers experiencing the most significant benefit. (2) When the manufacturer assumes a larger share of marketing costs for one channel (e.g., online or offline) and a smaller share for the other, the party receiving the higher cost-sharing proportion typically sees increased profitability, while the other party’s profitability may diminish. (3) Empirical analysis suggests that manufacturers should prioritise supporting online businesses’ marketing activities, as this strategy is more likely to result in higher overall profits for the manufacturer. (4) Interestingly, when equal cost-sharing proportions are offered to both online and offline enterprises for the sake of fairness, the manufacturer’s profitability is enhanced. Moreover, the profitability of online enterprises tends to increase when the equal cost-sharing proportion is smaller. These findings validate the proposed model and underscore the critical role of strategic cost-sharing contracts in optimising Online to Offline (O2O) supply chain performance. Further research could explore the implications of varying consumer preferences and digitalisation trends on the effectiveness of such strategies. ]]> Optimising O2O Supply Chain Strategies Through Cost-Sharing Contracts: Strategic Analysis and Empirical Insights fangfang guo zeyi yang wei qin yuanyuan wang siyi chen doi: 10.56578/jemse030401 Journal of Engineering Management and Systems Engineering 09-23-2024 Journal of Engineering Management and Systems Engineering 09-23-2024 2024 3 4 Article 183 10.56578/jemse030401 https://www.acadlore.com/article/JEMSE/2024_3_4/jemse030401 https://www.acadlore.com/article/JEMSE/2024_3_3/jemse030305 A detailed investigation into the axial bearing load of the revolving platform in a hydraulic excavator equipped with a shovel attachment was presented in this study. A mathematical model was formulated to assess the forces acting on the bearing under various operational conditions. The analysis focuses on a 100,000 kg excavator with a 6.5 m³ bucket, examining the contributions of kinematic chains and drive mechanisms to axial loads. Simulations of multiple positions within the working range were carried out, calculating the load spectrum, including boundary resistance, to ensure machine stability. An optimization program was developed to refine the bearing selection process by identifying equivalent loads and moments. These calculations were benchmarked against manufacturer capacity diagrams, allowing for precise selection of appropriate bearing sizes. The findings underscore the critical role of accurate load calculations in enhancing the performance, reliability, and design optimization of hydraulic excavators. This approach provides engineers with a framework for selecting bearings that can withstand complex operational stresses, thereby improving the efficiency and longevity of hydraulic machinery. 09-17-2024 <![CDATA[ A detailed investigation into the axial bearing load of the revolving platform in a hydraulic excavator equipped with a shovel attachment was presented in this study. A mathematical model was formulated to assess the forces acting on the bearing under various operational conditions. The analysis focuses on a 100,000 kg excavator with a 6.5 m³ bucket, examining the contributions of kinematic chains and drive mechanisms to axial loads. Simulations of multiple positions within the working range were carried out, calculating the load spectrum, including boundary resistance, to ensure machine stability. An optimization program was developed to refine the bearing selection process by identifying equivalent loads and moments. These calculations were benchmarked against manufacturer capacity diagrams, allowing for precise selection of appropriate bearing sizes. The findings underscore the critical role of accurate load calculations in enhancing the performance, reliability, and design optimization of hydraulic excavators. This approach provides engineers with a framework for selecting bearings that can withstand complex operational stresses, thereby improving the efficiency and longevity of hydraulic machinery. ]]> Load Spectrum Analysis of Axial Bearings in Hydraulic Excavators with Shovel Attachments vesna jovanović dragan marinković nikola petrović dubravko stojanović doi: 10.56578/jemse030305 Journal of Engineering Management and Systems Engineering 09-17-2024 Journal of Engineering Management and Systems Engineering 09-17-2024 2024 3 3 Article 175 10.56578/jemse030305 https://www.acadlore.com/article/JEMSE/2024_3_3/jemse030305 https://www.acadlore.com/article/JEMSE/2024_3_3/jemse030304 The current research is to profit from the science of enterprise architecture (Enterprise Architecture) and its application in building the structure of government sector institutions in the Kingdom of Saudi Arabia, in accordance with the Kingdom of Saudi Arabia 2030 vision. while emphasizing the value of enterprise architecture (EA) and the need for knowledge to apply its models and procedures while creating its structures. The research study's scope is determined by how well the descriptive and analytical approaches function together, and this is achieved by choosing a few government sector organizations to focus on. Throughout exploring the possibility of applying the Enterprise Architecture model, as an application case based on the extent of knowledge of the cadres of those entities with the organizations' enterprise architecture, and the presence of supervisory expertise. By relying on the quantitative method of studying and analyzing the situation by conducting a questionnaire on some workers in those bodies under consideration (Research Sample), studying the possibility and feasibility of applying enterprise architecture for organizations and generalizing this in the restructuring of government sector’s institutions in general. 08-26-2024 <![CDATA[ The current research is to profit from the science of enterprise architecture (Enterprise Architecture) and its application in building the structure of government sector institutions in the Kingdom of Saudi Arabia, in accordance with the Kingdom of Saudi Arabia 2030 vision. while emphasizing the value of enterprise architecture (EA) and the need for knowledge to apply its models and procedures while creating its structures. The research study's scope is determined by how well the descriptive and analytical approaches function together, and this is achieved by choosing a few government sector organizations to focus on. Throughout exploring the possibility of applying the Enterprise Architecture model, as an application case based on the extent of knowledge of the cadres of those entities with the organizations' enterprise architecture, and the presence of supervisory expertise. By relying on the quantitative method of studying and analyzing the situation by conducting a questionnaire on some workers in those bodies under consideration (Research Sample), studying the possibility and feasibility of applying enterprise architecture for organizations and generalizing this in the restructuring of government sector’s institutions in general. ]]> Utilizing the Enterprise Architecture Model to Develop the Structure of Public Sector Entities in Saudi Arabia mohamed i. youssef yaser m. hausawi doi: 10.56578/jemse030304 Journal of Engineering Management and Systems Engineering 08-26-2024 Journal of Engineering Management and Systems Engineering 08-26-2024 2024 3 3 Article 164 10.56578/jemse030304 https://www.acadlore.com/article/JEMSE/2024_3_3/jemse030304 https://www.acadlore.com/article/JEMSE/2024_3_3/jemse030303 This study evaluates the safety management system at Xuefu Gas Station in Xiangtan City of China through a combination of Preliminary Hazard Analysis (PHA) and Fault Tree Analysis (FTA). Initially, PHA was employed to identify potential hazards and assess the probability of associated accidents. This analysis led to the formulation of preventive measures aimed at mitigating identified risks. Subsequently, FTA was utilized to construct a logical framework for analyzing the various causes of system failures and their interdependencies. The analysis revealed deficiencies in the management system, equipment, ignition sources, and human factors. An approximate calculation method was applied to rank the structural importance of these factors, thereby highlighting key areas of impact. Based on these findings, targeted recommendations were proposed to enhance the safety management practices at the gas station, thereby reducing accident likelihood and safeguarding personnel and property. The results underscore the necessity of improving management practices, upgrading equipment, controlling ignition sources, and bolstering human factors to achieve a comprehensive safety management system. 08-18-2024 <![CDATA[ This study evaluates the safety management system at Xuefu Gas Station in Xiangtan City of China through a combination of Preliminary Hazard Analysis (PHA) and Fault Tree Analysis (FTA). Initially, PHA was employed to identify potential hazards and assess the probability of associated accidents. This analysis led to the formulation of preventive measures aimed at mitigating identified risks. Subsequently, FTA was utilized to construct a logical framework for analyzing the various causes of system failures and their interdependencies. The analysis revealed deficiencies in the management system, equipment, ignition sources, and human factors. An approximate calculation method was applied to rank the structural importance of these factors, thereby highlighting key areas of impact. Based on these findings, targeted recommendations were proposed to enhance the safety management practices at the gas station, thereby reducing accident likelihood and safeguarding personnel and property. The results underscore the necessity of improving management practices, upgrading equipment, controlling ignition sources, and bolstering human factors to achieve a comprehensive safety management system. ]]> Safety System Study of Gas Stations Based on Preliminary Hazard Analysis and Fault Tree Analysis qi li hai wu baoquan zhang zhihong dong tao ling doi: 10.56578/jemse030303 Journal of Engineering Management and Systems Engineering 08-18-2024 Journal of Engineering Management and Systems Engineering 08-18-2024 2024 3 3 Article 149 10.56578/jemse030303 https://www.acadlore.com/article/JEMSE/2024_3_3/jemse030303 https://www.acadlore.com/article/JEMSE/2024_3_3/jemse030302 The selection of appropriate anti-drone systems is critical for enhancing a military's defensive capabilities. With a range of non-kinetic anti-drone guns available, it is essential to identify the optimal system that meets specific military requirements. This study presents a comprehensive approach, combining Multiple Criteria Decision Making (MCDM) techniques to facilitate this selection process. The Defining Interrelationships Between Ranked Criteria II (DIBR II) method has been employed to determine and calculate the criteria weighting coefficients, while the Grey Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) method, modified to utilize interval grey numbers, has been applied to rank the alternatives. The criteria weighting coefficients, defined by expert input, are aggregated using the Bonferroni mean. The proposed DIBR II-Grey MARCOS model is then subjected to a sensitivity analysis, which further validates the robustness of the selection process. A comparative analysis of results, based on the applied MCDM methods, underscores the efficacy of the proposed model. The findings demonstrate that this integrated model not only provides a reliable framework for selecting anti-drone guns but also offers a versatile tool for resolving other MCDM challenges across various domains. The study highlights the potential of this model for broader application in diverse operational environments, where complex decision-making is required. The combination of MCDM techniques and sensitivity analysis offers valuable insights into optimizing resource allocation, thereby enhancing strategic decision-making processes. The proposed model's adaptability and effectiveness suggest its significant potential for adoption beyond the military sector. 08-18-2024 <![CDATA[ The selection of appropriate anti-drone systems is critical for enhancing a military's defensive capabilities. With a range of non-kinetic anti-drone guns available, it is essential to identify the optimal system that meets specific military requirements. This study presents a comprehensive approach, combining Multiple Criteria Decision Making (MCDM) techniques to facilitate this selection process. The Defining Interrelationships Between Ranked Criteria II (DIBR II) method has been employed to determine and calculate the criteria weighting coefficients, while the Grey Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) method, modified to utilize interval grey numbers, has been applied to rank the alternatives. The criteria weighting coefficients, defined by expert input, are aggregated using the Bonferroni mean. The proposed DIBR II-Grey MARCOS model is then subjected to a sensitivity analysis, which further validates the robustness of the selection process. A comparative analysis of results, based on the applied MCDM methods, underscores the efficacy of the proposed model. The findings demonstrate that this integrated model not only provides a reliable framework for selecting anti-drone guns but also offers a versatile tool for resolving other MCDM challenges across various domains. The study highlights the potential of this model for broader application in diverse operational environments, where complex decision-making is required. The combination of MCDM techniques and sensitivity analysis offers valuable insights into optimizing resource allocation, thereby enhancing strategic decision-making processes. The proposed model's adaptability and effectiveness suggest its significant potential for adoption beyond the military sector. ]]> Optimization of Anti-Drone Defense: Analyzing Non-Kinetic Gun Selection Using DIBR II-Grey MARCOS Methodology marko radovanović marko crnogorac stefan jovčić elif cirkin mouhamed bayane bouraima doi: 10.56578/jemse030302 Journal of Engineering Management and Systems Engineering 08-18-2024 Journal of Engineering Management and Systems Engineering 08-18-2024 2024 3 3 Article 132 10.56578/jemse030302 https://www.acadlore.com/article/JEMSE/2024_3_3/jemse030302 https://www.acadlore.com/article/JEMSE/2024_3_3/jemse030301 To address the challenges in traditional Failure Mode and Effects Analysis (FMEA) related to determining factor weights, identifying risk priority of failure modes, and managing uncertainties in the risk assessment process, this paper proposes an enhanced FMEA risk factor evaluation method. This method integrates incomplete and imprecise expert assessments using a fuzzy multi-criteria compromise ranking technique called the “V1seKriterijumska Optimizacija I Kompromisno Resenje” (VIKOR). By employing Fuzzy Evidence Reasoning (FER), the risk factor ratings are represented using fuzzy belief structures to capture their diversity and uncertainty. Objective weights are adjusted using Shannon entropy to correct subjective weights, and the VIKOR technique is applied to prioritize failure modes based on the principles of minimizing individual regret and maximizing group utility. The improved model is applied to identify key equipment associated with oil and gas leakage risk in the Floating Production Storage and Offloading (FPSO) system. Validity and sensitivity analysis confirm the robustness and reliability of the method, enhancing the accuracy and credibility of the evaluation results. 08-13-2024 <![CDATA[ <p>To address the challenges in traditional Failure Mode and Effects Analysis (FMEA) related to determining factor weights, identifying risk priority of failure modes, and managing uncertainties in the risk assessment process, this paper proposes an enhanced FMEA risk factor evaluation method. This method integrates incomplete and imprecise expert assessments using a fuzzy multi-criteria compromise ranking technique called the “V1seKriterijumska Optimizacija I Kompromisno Resenje” (VIKOR). By employing Fuzzy Evidence Reasoning (FER), the risk factor ratings are represented using fuzzy belief structures to capture their diversity and uncertainty. Objective weights are adjusted using Shannon entropy to correct subjective weights, and the VIKOR technique is applied to prioritize failure modes based on the principles of minimizing individual regret and maximizing group utility. The improved model is applied to identify key equipment associated with oil and gas leakage risk in the Floating Production Storage and Offloading (FPSO) system. Validity and sensitivity analysis confirm the robustness and reliability of the method, enhancing the accuracy and credibility of the evaluation results.</p> ]]> An Enhanced Failure Mode and Effects Analysis Risk Identification Method Based on Uncertainty and Fuzziness longting wang yanqun yu zimo liu zhihui liu xiuquan liu doi: 10.56578/jemse030301 Journal of Engineering Management and Systems Engineering 08-13-2024 Journal of Engineering Management and Systems Engineering 08-13-2024 2024 3 3 Article 116 10.56578/jemse030301 https://www.acadlore.com/article/JEMSE/2024_3_3/jemse030301 https://www.acadlore.com/article/JEMSE/2024_3_2/jemse030205 New aggregation operators (AOs) for interval-valued intuitionistic fuzzy sets (IVIFS) have been developed, offering advancements in multi-attribute group decision-making (MAGDM). IVIFS employs intervals for membership and non-membership grades, providing a robust framework to handle uncertainties inherent in real-world scenarios. This study introduces operational laws for interval-valued intuitionistic fuzzy values (IVIFVs), formulated on the Frank T-norm and T-conorm, and presents a generalization of the Maclaurin symmetric mean (MSM) operator tailored for these values. Named the interval-valued intuitionistic fuzzy Frank weighted MSM (IVIFFWMSM) and interval-valued intuitionistic fuzzy Frank MSM (IVIFFMSM), these operators incorporate new operational principles that enhance the aggregation process. The effectiveness of these operators is demonstrated through their application to a MAGDM problem, where they are compared with existing operators. This approach not only enriches the theoretical landscape of fuzzy decision-making models but also provides practical insights into the optimization of market risk. 05-30-2024 <![CDATA[ New aggregation operators (AOs) for interval-valued intuitionistic fuzzy sets (IVIFS) have been developed, offering advancements in multi-attribute group decision-making (MAGDM). IVIFS employs intervals for membership and non-membership grades, providing a robust framework to handle uncertainties inherent in real-world scenarios. This study introduces operational laws for interval-valued intuitionistic fuzzy values (IVIFVs), formulated on the Frank T-norm and T-conorm, and presents a generalization of the Maclaurin symmetric mean (MSM) operator tailored for these values. Named the interval-valued intuitionistic fuzzy Frank weighted MSM (IVIFFWMSM) and interval-valued intuitionistic fuzzy Frank MSM (IVIFFMSM), these operators incorporate new operational principles that enhance the aggregation process. The effectiveness of these operators is demonstrated through their application to a MAGDM problem, where they are compared with existing operators. This approach not only enriches the theoretical landscape of fuzzy decision-making models but also provides practical insights into the optimization of market risk. ]]> Optimization of Market Risk via Maclaurin Symmetric Mean Aggregation Operators: An Application of Interval-Valued Intuitionistic Fuzzy Sets in Multi-Attribute Group Decision-Making mehwish sarfraz darko božanić doi: 10.56578/jemse030205 Journal of Engineering Management and Systems Engineering 05-30-2024 Journal of Engineering Management and Systems Engineering 05-30-2024 2024 3 2 Article 100 10.56578/jemse030205 https://www.acadlore.com/article/JEMSE/2024_3_2/jemse030205 https://www.acadlore.com/article/JEMSE/2024_3_2/jemse030204 Computer-Aided Design (CAD) is employed extensively to facilitate design processes through software tools, serving as an indispensable component in Reverse Engineering (RE) across various sectors. This study elucidates the integration of RE and CAD in constructing generic product models for the manufacturing industry, particularly through the enhancement of the Feature-Based Design (FBD) approach. The Characteristic Product Features (CPF) methodology, pivotal in this research, enhances FBD by enabling the creation of parametrically defined generic features. Such features encapsulate a range of parameters including geometrical dimensions, topological constraints, and requirements for material properties and functionality, all dictated by the parametric model established. The methodology affords mechanical engineers enhanced capabilities to devise specific or customized manufacturing processes, applicable in domains spanning CAD, Computer-Aided Manufacturing (CAM), and Computer-Aided Engineering (CAE). The practical application of CPF within CAD is exemplified through the development of a three-dimensional geometrical model of an extruder screw utilized in polymer extrusion, illustrating the potential for tailored process innovation in manufacturing. 05-29-2024 <![CDATA[ Computer-Aided Design (CAD) is employed extensively to facilitate design processes through software tools, serving as an indispensable component in Reverse Engineering (RE) across various sectors. This study elucidates the integration of RE and CAD in constructing generic product models for the manufacturing industry, particularly through the enhancement of the Feature-Based Design (FBD) approach. The Characteristic Product Features (CPF) methodology, pivotal in this research, enhances FBD by enabling the creation of parametrically defined generic features. Such features encapsulate a range of parameters including geometrical dimensions, topological constraints, and requirements for material properties and functionality, all dictated by the parametric model established. The methodology affords mechanical engineers enhanced capabilities to devise specific or customized manufacturing processes, applicable in domains spanning CAD, Computer-Aided Manufacturing (CAM), and Computer-Aided Engineering (CAE). The practical application of CPF within CAD is exemplified through the development of a three-dimensional geometrical model of an extruder screw utilized in polymer extrusion, illustrating the potential for tailored process innovation in manufacturing. ]]> Geometrical Modeling of Extruder Screws Utilizing the Characteristic Product Features Method in CAD nikola vitkovic miodrag manic sasa randjelovic nikola korunovic rajko turudija aleksandar trajkovic jovan arandjelovic doi: 10.56578/jemse030204 Journal of Engineering Management and Systems Engineering 05-29-2024 Journal of Engineering Management and Systems Engineering 05-29-2024 2024 3 2 Article 93 10.56578/jemse030204 https://www.acadlore.com/article/JEMSE/2024_3_2/jemse030204 https://www.acadlore.com/article/JEMSE/2024_3_2/jemse030203 In this study, an economical prototype of a uniaxial shake table, named the Eastern Mediterranean University (EMU) shake table, was developed using an Arduino platform for the simulation of sinusoidal waves and scaled earthquake data. The table incorporates a ball-screw mechanism actuated by a stepper motor. Simulations were conducted using sinusoidal signals and earthquake data for three distinct seismic events, recorded at discrete timestamps. The performance of the shake table was assessed by analyzing the discrepancies between the input signals and the table's outputs.In sinusoidal mode, a feedforward gain was computed to achieve the desired output amplitude values. Furthermore, a decreasing trend in the error between input and output acceleration values was observed. The table, without any payload, achieved an acceleration of 0.8 g at a frequency of 14.5 Hz and an amplitude of 1 mm. However, the effectiveness of earthquake simulations was constrained by the storage capacity of the Arduino Uno and the motor's performance capacity. Iterative methods were necessary for each earthquake simulation to determine the minimal timestep size that the motor could optimally handle. The methodology for simulating earthquakes was elaborated, identifying limitations and suggesting areas for future enhancement. The major constraints of the project were cost, time, and resource availability. 05-28-2024 <![CDATA[ In this study, an economical prototype of a uniaxial shake table, named the Eastern Mediterranean University (EMU) shake table, was developed using an Arduino platform for the simulation of sinusoidal waves and scaled earthquake data. The table incorporates a ball-screw mechanism actuated by a stepper motor. Simulations were conducted using sinusoidal signals and earthquake data for three distinct seismic events, recorded at discrete timestamps. The performance of the shake table was assessed by analyzing the discrepancies between the input signals and the table's outputs.In sinusoidal mode, a feedforward gain was computed to achieve the desired output amplitude values. Furthermore, a decreasing trend in the error between input and output acceleration values was observed. The table, without any payload, achieved an acceleration of 0.8 g at a frequency of 14.5 Hz and an amplitude of 1 mm. However, the effectiveness of earthquake simulations was constrained by the storage capacity of the Arduino Uno and the motor's performance capacity. Iterative methods were necessary for each earthquake simulation to determine the minimal timestep size that the motor could optimally handle. The methodology for simulating earthquakes was elaborated, identifying limitations and suggesting areas for future enhancement. The major constraints of the project were cost, time, and resource availability. ]]> Development and Evaluation of an Economical Arduino-Based Uniaxial Shake Table for Earthquake and Wave Simulation mirza dawood baig ahmed murad abdulrazzaq saif osinachi mbah umut yildirim görkem ozankaya qasim zeeshan doi: 10.56578/jemse030203 Journal of Engineering Management and Systems Engineering 05-28-2024 Journal of Engineering Management and Systems Engineering 05-28-2024 2024 3 2 Article 82 10.56578/jemse030203 https://www.acadlore.com/article/JEMSE/2024_3_2/jemse030203 https://www.acadlore.com/article/JEMSE/2024_3_2/jemse030202 In the pursuit of sustainable urban development, the implementation of cleaner propulsion systems in public transportation emerges as a critical strategy to reduce urban pollution and emissions. This study focuses on the City of Niš, where conventional propulsion vehicles, predominantly buses, contribute significantly to environmental degradation. The necessity to adopt alternative propulsion systems is underscored by the myriad of limitations and uncertainties that accompany such a transition. To address this complexity, the criteria importance through intercriteria correlation (CRITIC) method was employed to derive weight coefficients, while the evaluation based on distance from average solution (EDAS) method was utilized to select optimal propulsion systems. These methodologies facilitated a comprehensive evaluation of alternatives, including buses, electric trolleybuses, and trams, for both city and suburban public transport. The integration of these multi-criteria decision-making techniques enabled a systematic analysis of each alternative against established criteria, thereby assisting in the identification of the most advantageous propulsion systems. This approach not only aids in making informed decisions that align with sustainability objectives but also contributes significantly to mitigating the environmental impact of urban transport. The findings from this study provide a foundational framework that supports decision-makers in the strategic implementation of environmentally sustainable transport solutions in urban settings. 05-27-2024 <![CDATA[ In the pursuit of sustainable urban development, the implementation of cleaner propulsion systems in public transportation emerges as a critical strategy to reduce urban pollution and emissions. This study focuses on the City of Niš, where conventional propulsion vehicles, predominantly buses, contribute significantly to environmental degradation. The necessity to adopt alternative propulsion systems is underscored by the myriad of limitations and uncertainties that accompany such a transition. To address this complexity, the criteria importance through intercriteria correlation (CRITIC) method was employed to derive weight coefficients, while the evaluation based on distance from average solution (EDAS) method was utilized to select optimal propulsion systems. These methodologies facilitated a comprehensive evaluation of alternatives, including buses, electric trolleybuses, and trams, for both city and suburban public transport. The integration of these multi-criteria decision-making techniques enabled a systematic analysis of each alternative against established criteria, thereby assisting in the identification of the most advantageous propulsion systems. This approach not only aids in making informed decisions that align with sustainability objectives but also contributes significantly to mitigating the environmental impact of urban transport. The findings from this study provide a foundational framework that supports decision-makers in the strategic implementation of environmentally sustainable transport solutions in urban settings. ]]> Evaluating Alternative Propulsion Systems for Urban Public Transport in Niš: A Multicriteria Decision-Making Approach nikola petrović saša marković boban nikolić vesna jovanović marijana petrović doi: 10.56578/jemse030202 Journal of Engineering Management and Systems Engineering 05-27-2024 Journal of Engineering Management and Systems Engineering 05-27-2024 2024 3 2 Article 72 10.56578/jemse030202 https://www.acadlore.com/article/JEMSE/2024_3_2/jemse030202 https://www.acadlore.com/article/JEMSE/2024_3_2/jemse030201 Effective management of supply chains, pivotal for sustaining business operations, is increasingly challenged by rising costs and complexity in logistics processes. Performance-Based Logistics (PBL) emerges as a critical strategy to enhance logistical effectiveness and competitiveness by focusing on performance targets rather than merely procuring products or services for maintenance and repair. This study examines the implementation of PBL in manufacturing enterprises and explores the factors influencing its benefits. By employing the polytopic fuzzy Stepwise Weight Assessment Ratio Analysis (SWARA) method, a sophisticated Multi-criteria Decision Analysis (MCDA) technique, criteria were weighted to determine their impact on PBL effectiveness. It was found that the paramount criterion affecting PBL advantages is the capability to manage operations more effectively, whereas the reduction in system lifecycle costs through savings in labor and training was identified as the least impactful. This analysis not only underscores the necessity of designing reliable systems that align with customer expectations but also highlights the added value PBL provides by integrating reduced support elements essential for logistics and sustainability. The findings advocate for meticulous emphasis on PBL practices within business models to optimize operational efficiency and strategic advantage. 05-06-2024 <![CDATA[ Effective management of supply chains, pivotal for sustaining business operations, is increasingly challenged by rising costs and complexity in logistics processes. Performance-Based Logistics (PBL) emerges as a critical strategy to enhance logistical effectiveness and competitiveness by focusing on performance targets rather than merely procuring products or services for maintenance and repair. This study examines the implementation of PBL in manufacturing enterprises and explores the factors influencing its benefits. By employing the polytopic fuzzy Stepwise Weight Assessment Ratio Analysis (SWARA) method, a sophisticated Multi-criteria Decision Analysis (MCDA) technique, criteria were weighted to determine their impact on PBL effectiveness. It was found that the paramount criterion affecting PBL advantages is the capability to manage operations more effectively, whereas the reduction in system lifecycle costs through savings in labor and training was identified as the least impactful. This analysis not only underscores the necessity of designing reliable systems that align with customer expectations but also highlights the added value PBL provides by integrating reduced support elements essential for logistics and sustainability. The findings advocate for meticulous emphasis on PBL practices within business models to optimize operational efficiency and strategic advantage. ]]> Evaluating Performance-Based Logistics in Manufacturing Through Polytopic Fuzzy SWARA: A Criterion Assessment Approach ahmet aytekin selçuk korucuk doi: 10.56578/jemse030201 Journal of Engineering Management and Systems Engineering 05-06-2024 Journal of Engineering Management and Systems Engineering 05-06-2024 2024 3 2 Article 65 10.56578/jemse030201 https://www.acadlore.com/article/JEMSE/2024_3_2/jemse030201 https://www.acadlore.com/article/JEMSE/2024_3_1/jemse030105 This investigation delves into the noise attenuation capabilities of an innovatively designed muffler, which integrates additional piping and perforation to augment sound reflection. The enhanced muffler's design was rigorously simulated using the Helmholtz equation through the application of COMSOL Multiphysics software, aiming to delineate its acoustic performance relative to conventional models. The analysis underscored the superior efficacy of the optimized model in elevating transmission loss, diminishing acoustic pressure, and concurrently attenuating noise and frequency levels. A comparative evaluation of the transmission loss between the traditional and the novel muffler revealed a significant amelioration in the latter, highlighting its advanced noise reduction capabilities. The study further illuminated that exhaust pressure and back pressure contribute to acoustic wave generation, prompting the optimization of the muffler design to mitigate pressure, thereby circumventing potential damage. Notably, despite the analytical complexity, the construction of the proposed muffler remains straightforward, representing a pivotal advantage. This research contributes to the acoustic engineering field by presenting a muffler design that not only significantly reduces noise pollution but also demonstrates an ease of construction, making it a viable solution for widespread application. The findings advocate for the muffler's potential in enhancing acoustic comfort and environmental compliance in automotive and industrial settings. 03-04-2024 <![CDATA[ This investigation delves into the noise attenuation capabilities of an innovatively designed muffler, which integrates additional piping and perforation to augment sound reflection. The enhanced muffler's design was rigorously simulated using the Helmholtz equation through the application of COMSOL Multiphysics software, aiming to delineate its acoustic performance relative to conventional models. The analysis underscored the superior efficacy of the optimized model in elevating transmission loss, diminishing acoustic pressure, and concurrently attenuating noise and frequency levels. A comparative evaluation of the transmission loss between the traditional and the novel muffler revealed a significant amelioration in the latter, highlighting its advanced noise reduction capabilities. The study further illuminated that exhaust pressure and back pressure contribute to acoustic wave generation, prompting the optimization of the muffler design to mitigate pressure, thereby circumventing potential damage. Notably, despite the analytical complexity, the construction of the proposed muffler remains straightforward, representing a pivotal advantage. This research contributes to the acoustic engineering field by presenting a muffler design that not only significantly reduces noise pollution but also demonstrates an ease of construction, making it a viable solution for widespread application. The findings advocate for the muffler's potential in enhancing acoustic comfort and environmental compliance in automotive and industrial settings. ]]> Enhanced Acoustic Attenuation Performance of a Novel Absorptive Muffler: A Helmholtz Equation-Based Simulation Study youssef el chami zahra pezeshki sidi mohamed sidi mohamed babak safaei doi: 10.56578/jemse030105 Journal of Engineering Management and Systems Engineering 03-04-2024 Journal of Engineering Management and Systems Engineering 03-04-2024 2024 3 1 Article 53 10.56578/jemse030105 https://www.acadlore.com/article/JEMSE/2024_3_1/jemse030105 https://www.acadlore.com/article/JEMSE/2024_3_1/jemse030104 The Spherical Fuzzy Set (SFS) framework extends the Picture Fuzzy Set (PFS) concept, offering enhanced precision in handling data characterized by conflict and uncertainty. Furthermore, similarity measures (SMs) are crucial for determining the extent of resemblance between pairs of fuzzy values. While existing SMs evaluate similarity by measuring the distance between values, they sometimes yield results that are illogical or unreasonable, due to certain properties and operational complexities. To address these anomalies, this paper introduces a parametric similarity measure based on three adjustable parameters ($\alpha_1, \alpha_2, \alpha_3$), allowing decision-makers to fine-tune the measure to suit various decision-making styles. This paper also scrutinizes existing SMs from a mathematical standpoint and demonstrates the efficacy of the proposed SM through mathematical modeling. Finally, we apply the proposed SM to tackle Multi-Attribute Decision-Making (MADM) problems. Comparative analysis reveals that our proposed SM outperforms certain existing SMs in the context of SFS-based applications. 02-19-2024 <![CDATA[ The Spherical Fuzzy Set (SFS) framework extends the Picture Fuzzy Set (PFS) concept, offering enhanced precision in handling data characterized by conflict and uncertainty. Furthermore, similarity measures (SMs) are crucial for determining the extent of resemblance between pairs of fuzzy values. While existing SMs evaluate similarity by measuring the distance between values, they sometimes yield results that are illogical or unreasonable, due to certain properties and operational complexities. To address these anomalies, this paper introduces a parametric similarity measure based on three adjustable parameters ($\alpha_1, \alpha_2, \alpha_3$), allowing decision-makers to fine-tune the measure to suit various decision-making styles. This paper also scrutinizes existing SMs from a mathematical standpoint and demonstrates the efficacy of the proposed SM through mathematical modeling. Finally, we apply the proposed SM to tackle Multi-Attribute Decision-Making (MADM) problems. Comparative analysis reveals that our proposed SM outperforms certain existing SMs in the context of SFS-based applications. ]]> A Parametric Similarity Measure for Spherical Fuzzy Sets and Its Applications in Medical Equipment Selection mehwish sarfraz dragan pamucar doi: 10.56578/jemse030104 Journal of Engineering Management and Systems Engineering 02-19-2024 Journal of Engineering Management and Systems Engineering 02-19-2024 2024 3 1 Article 38 10.56578/jemse030104 https://www.acadlore.com/article/JEMSE/2024_3_1/jemse030104 https://www.acadlore.com/article/JEMSE/2024_3_1/jemse030103 This research employs a data-driven approach to optimize the plasma arc cutting process. The evaluation of cut quality is based on six output characteristics, while the input parameters include stand-off distance, cutting current, and cutting speed. The output metrics consist of the material removal rate (MRR), surface roughness, bevel angle, slag formation, kerf width, and heat-affected zone (HAZ). Given the complexity of the process and the multitude of involved processing parameters, it is imperative to develop an optimization model to ensure the production of undisturbed structures. The primary aim of this study is to identify the most critical factors that facilitate optimal conditions for plasma arc cutting. The research goal is to determine the influence of input parameters on the plasma arc cutting quality using an adaptive neural fuzzy inference system (ANFIS). It has been found that the material removal rate (MRR), surface roughness, bevel angle, slag formation, kerf width, and heat-affected zone (HAZ) are predominantly affected by the interplay of cutting current and stand-off distance. Ideally, the best predictive model for various attributes, such as MRR, bevel angle, slag formation, surface roughness, kerf width, and HAZ, is one that synergistically combines cutting current and stand-off distance. This study, which evaluates multiple input parameters simultaneously, is expected to attract significant attention as it represents a pioneering small-scale investigation in the field. 01-31-2024 <![CDATA[ This research employs a data-driven approach to optimize the plasma arc cutting process. The evaluation of cut quality is based on six output characteristics, while the input parameters include stand-off distance, cutting current, and cutting speed. The output metrics consist of the material removal rate (MRR), surface roughness, bevel angle, slag formation, kerf width, and heat-affected zone (HAZ). Given the complexity of the process and the multitude of involved processing parameters, it is imperative to develop an optimization model to ensure the production of undisturbed structures. The primary aim of this study is to identify the most critical factors that facilitate optimal conditions for plasma arc cutting. The research goal is to determine the influence of input parameters on the plasma arc cutting quality using an adaptive neural fuzzy inference system (ANFIS). It has been found that the material removal rate (MRR), surface roughness, bevel angle, slag formation, kerf width, and heat-affected zone (HAZ) are predominantly affected by the interplay of cutting current and stand-off distance. Ideally, the best predictive model for various attributes, such as MRR, bevel angle, slag formation, surface roughness, kerf width, and HAZ, is one that synergistically combines cutting current and stand-off distance. This study, which evaluates multiple input parameters simultaneously, is expected to attract significant attention as it represents a pioneering small-scale investigation in the field. ]]> Optimization of the Plasma Arc Cutting Process Through Technological Forecasting miloš milovančević kamen boyanov spasov abouzar rahimi doi: 10.56578/jemse030103 Journal of Engineering Management and Systems Engineering 01-31-2024 Journal of Engineering Management and Systems Engineering 01-31-2024 2024 3 1 Article 30 10.56578/jemse030103 https://www.acadlore.com/article/JEMSE/2024_3_1/jemse030103 https://www.acadlore.com/article/JEMSE/2024_3_1/jemse030102 In the field of industrial buildings, notably within warehouse settings, the optimization of floor space emerges as a paramount concern. The deployment of equipment facilitating continuous transport is mandated to not only augment throughput but also to economize on spatial allocation. Within this spectrum, continuous vertical conveyors, particularly of the paternoster variety, have been adopted as a quintessential solution. This study delineates the design intricacies of a paternoster continuous vertical conveyor, elucidating the methodology employed in calculating its maximal throughput, movement resistance, and the requisite power for its electric motor. Through a rigorous analytical approach, the performance of the paternoster conveyor is meticulously evaluated and juxtaposed against alternative continuous vertical conveyor systems. The findings underscore the paternoster conveyor's efficacy in achieving high throughput efficiency while conserving space, thus reaffirming its utility in industrial warehousing. The evaluation employs comparative metrics to highlight the paternoster system's superiority in specific operational parameters. This analysis contributes to the corpus of knowledge by providing a comprehensive examination of paternoster conveyors, thereby aiding in the selection of efficient transport solutions within the constraints of warehouse space optimization. 01-30-2024 <![CDATA[ In the field of industrial buildings, notably within warehouse settings, the optimization of floor space emerges as a paramount concern. The deployment of equipment facilitating continuous transport is mandated to not only augment throughput but also to economize on spatial allocation. Within this spectrum, continuous vertical conveyors, particularly of the paternoster variety, have been adopted as a quintessential solution. This study delineates the design intricacies of a paternoster continuous vertical conveyor, elucidating the methodology employed in calculating its maximal throughput, movement resistance, and the requisite power for its electric motor. Through a rigorous analytical approach, the performance of the paternoster conveyor is meticulously evaluated and juxtaposed against alternative continuous vertical conveyor systems. The findings underscore the paternoster conveyor's efficacy in achieving high throughput efficiency while conserving space, thus reaffirming its utility in industrial warehousing. The evaluation employs comparative metrics to highlight the paternoster system's superiority in specific operational parameters. This analysis contributes to the corpus of knowledge by providing a comprehensive examination of paternoster conveyors, thereby aiding in the selection of efficient transport solutions within the constraints of warehouse space optimization. ]]> Efficacy and Space Optimization in Industrial Warehouses: An Evaluation of Paternoster Continuous Vertical Conveyors saša marković nikola petrović boban nikolić jelena mihajlović doi: 10.56578/jemse030102 Journal of Engineering Management and Systems Engineering 01-30-2024 Journal of Engineering Management and Systems Engineering 01-30-2024 2024 3 1 Article 21 10.56578/jemse030102 https://www.acadlore.com/article/JEMSE/2024_3_1/jemse030102 https://www.acadlore.com/article/JEMSE/2024_3_1/jemse030101 Against the backdrop of current market demands for a variety of products in small batches, traditional single-variety assembly lines are transitioning to variable production lines to accommodate the manufacturing of multiple similar products. This paper discusses the production unit as a microcosm of the variable production line, which boasts advantages such as smaller line scale, short setup times for changeovers, and ease of product scheduling. A mathematical model for splitting variable production lines into production units is established, with solutions at two levels: resource allocation and product scheduling. The upper-level model focuses on determining the number of production units and the distribution scheme of operators and equipment across multiple channels; the lower-level model addresses the product allocation problem, which is characterized by multiple stages, divisibility, variable batch sizes, and minimum batch size constraints. The solution approaches include a branch and bound method for small-scale problems to obtain optimal solutions, and an improved particle swarm optimization (PSO) algorithm for medium to large-scale problems to find near-optimal solutions. The innovation of the paper lies in the construction of the variable production line splitting model and the optimization algorithms for resource allocation and product scheduling. 01-19-2024 <![CDATA[ Against the backdrop of current market demands for a variety of products in small batches, traditional single-variety assembly lines are transitioning to variable production lines to accommodate the manufacturing of multiple similar products. This paper discusses the production unit as a microcosm of the variable production line, which boasts advantages such as smaller line scale, short setup times for changeovers, and ease of product scheduling. A mathematical model for splitting variable production lines into production units is established, with solutions at two levels: resource allocation and product scheduling. The upper-level model focuses on determining the number of production units and the distribution scheme of operators and equipment across multiple channels; the lower-level model addresses the product allocation problem, which is characterized by multiple stages, divisibility, variable batch sizes, and minimum batch size constraints. The solution approaches include a branch and bound method for small-scale problems to obtain optimal solutions, and an improved particle swarm optimization (PSO) algorithm for medium to large-scale problems to find near-optimal solutions. The innovation of the paper lies in the construction of the variable production line splitting model and the optimization algorithms for resource allocation and product scheduling. ]]> An Investigation into Multi-Stage, Variable-Batch Scheduling Across Multiple Production Units yi du t. k. satish kumar yongqiang wang jialin wang doi: 10.56578/jemse030101 Journal of Engineering Management and Systems Engineering 01-19-2024 Journal of Engineering Management and Systems Engineering 01-19-2024 2024 3 1 Article 1 10.56578/jemse030101 https://www.acadlore.com/article/JEMSE/2024_3_1/jemse030101 https://www.acadlore.com/article/JEMSE/2023_2_4/jemse020405 In the realm of smart vehicle navigation, both in known and unknown environments, the crucial aspects encompass the vehicle's localization using an array of technologies such as GPS, cameras, vision systems, laser, and ultrasonic sensors. This process is pivotal for effective motion planning within the vehicle's free configuration space, enabling it to adeptly avoid obstacles. The focal point of such navigation systems lies in devising a path from an initial to a target configuration, striving to minimize the path length and the time taken, while simultaneously circumventing obstacles. The application of metaheuristic algorithms has been pivotal in this regard. These algorithms, characterized by their ability to exploit initial solutions and explore the environment for feasible pathways, have been extensively utilized. A significant body of research in robotics and automation has focused on evaluating the efficacy of population-based algorithms including Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Firefly Algorithm (FA), and Whale Optimization Algorithm (WOA). Additionally, trajectory-based methods such as Tabu Search (TS) and Simulated Annealing (SA) have been scrutinized for their proficiency in identifying short, feasible paths among the plethora of solutions. There has been a surge in the enhancement and modification of these algorithms, with a multitude of hybrid metaheuristic algorithms being proposed. This review meticulously examines various metaheuristic algorithms and their hybridizations, specifically in their application to the path planning challenges faced by smart vehicles. The exploration extends to the comparison of these algorithms, highlighting their distinct advantages and limitations. Furthermore, the review delves into potential future directions in this evolving field, emphasizing the continual refinement of these algorithms to cater to the increasingly complex demands of smart vehicle navigation. 12-28-2023 <![CDATA[ In the realm of smart vehicle navigation, both in known and unknown environments, the crucial aspects encompass the vehicle's localization using an array of technologies such as GPS, cameras, vision systems, laser, and ultrasonic sensors. This process is pivotal for effective motion planning within the vehicle's free configuration space, enabling it to adeptly avoid obstacles. The focal point of such navigation systems lies in devising a path from an initial to a target configuration, striving to minimize the path length and the time taken, while simultaneously circumventing obstacles. The application of metaheuristic algorithms has been pivotal in this regard. These algorithms, characterized by their ability to exploit initial solutions and explore the environment for feasible pathways, have been extensively utilized. A significant body of research in robotics and automation has focused on evaluating the efficacy of population-based algorithms including Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Firefly Algorithm (FA), and Whale Optimization Algorithm (WOA). Additionally, trajectory-based methods such as Tabu Search (TS) and Simulated Annealing (SA) have been scrutinized for their proficiency in identifying short, feasible paths among the plethora of solutions. There has been a surge in the enhancement and modification of these algorithms, with a multitude of hybrid metaheuristic algorithms being proposed. This review meticulously examines various metaheuristic algorithms and their hybridizations, specifically in their application to the path planning challenges faced by smart vehicles. The exploration extends to the comparison of these algorithms, highlighting their distinct advantages and limitations. Furthermore, the review delves into potential future directions in this evolving field, emphasizing the continual refinement of these algorithms to cater to the increasingly complex demands of smart vehicle navigation. ]]> Optimizing Path Planning for Smart Vehicles: A Comprehensive Review of Metaheuristic Algorithms osinachi mbah qasim zeeshan doi: 10.56578/jemse020405 Journal of Engineering Management and Systems Engineering 12-28-2023 Journal of Engineering Management and Systems Engineering 12-28-2023 2023 2 4 Article 231 10.56578/jemse020405 https://www.acadlore.com/article/JEMSE/2023_2_4/jemse020405 https://www.acadlore.com/article/JEMSE/2023_2_4/jemse020404 The burgeoning importance of digitalization and cyber-physical manufacturing systems in the industrial sector is undeniable, yet discussions around viable solutions for small and medium-sized enterprises remain scant. These enterprises often face constraints in replacing extant machinery or implementing extensive IT upgrades, despite the availability of skilled engineering personnel. In response to this gap, an illustrative use case involving the application of Digital Twins (DT) to legacy systems is delineated, encompassing a detailed exploration of necessary hardware and software components, alongside pertinent considerations for implementation design. The establishment of a symbiotic relationship between the physical and digital realms is underscored as imperative, necessitating a granular understanding of the system to uncover opportunities and constraints for intervention. Such understanding is posited as a critical determinant of the DT's utility. This case study, situated within the Cyber-Physical Manufacturing Systems Laboratory at Széchenyi István University, serves to elucidate these principles and contribute to the discourse on smart manufacturing solutions for legacy systems. 11-06-2023 <![CDATA[ <p>The burgeoning importance of digitalization and cyber-physical manufacturing systems in the industrial sector is undeniable, yet discussions around viable solutions for small and medium-sized enterprises remain scant. These enterprises often face constraints in replacing extant machinery or implementing extensive IT upgrades, despite the availability of skilled engineering personnel. In response to this gap, an illustrative use case involving the application of Digital Twins (DT) to legacy systems is delineated, encompassing a detailed exploration of necessary hardware and software components, alongside pertinent considerations for implementation design. The establishment of a symbiotic relationship between the physical and digital realms is underscored as imperative, necessitating a granular understanding of the system to uncover opportunities and constraints for intervention. Such understanding is posited as a critical determinant of the DT's utility. This case study, situated within the Cyber-Physical Manufacturing Systems Laboratory at Széchenyi István University, serves to elucidate these principles and contribute to the discourse on smart manufacturing solutions for legacy systems.</p> ]]> Enabling Legacy Lab-Scale Production Systems: A Digital Twin Approach at Széchenyi István University gergő dávid monek norbert szántó richárd korpai szabolcs kocsis szürke szabolcs fischer doi: 10.56578/jemse020404 Journal of Engineering Management and Systems Engineering 11-06-2023 Journal of Engineering Management and Systems Engineering 11-06-2023 2023 2 4 Article 224 10.56578/jemse020404 https://www.acadlore.com/article/JEMSE/2023_2_4/jemse020404 https://www.acadlore.com/article/JEMSE/2023_2_4/jemse020403 In military operations, the proficient overcoming of water barriers is paramount, with sub-optimal execution potentially leading to significant human and equipment casualties. In this context, global armed forces accord considerable emphasis to the selection of appropriate mechanisms for water obstacle overcoming. This study elucidates the adoption of a Multi-Criteria Decision-Making (MCDM) approach for the selection of optimal pontoon bridge sets for military applications. Criteria identification was undertaken by seven distinguished experts, leading to the determination of weight coefficients using the Defining Interrelationships Between Ranked criteria II (DIBR II) method. Expert assessments were subsequently aggregated utilizing the Normalized Weighted Bonferroni Mean (NWBM) operator. The Multi-Attributive Ideal-Real Comparative Analysis (MAIRCA) method, operationalized within the Fermatean Fuzzy (FF) environment, was harnessed for the discernment of the best alternative. An analysis of the sensitivity of the study's findings with respect to variations in criteria weighting, coupled with a comparative exploration, led to the inference that the proposed MCDM model boasts stability. However, it was noted that the model exhibits sensitivity to shifts in criteria weight coefficients, underscoring its utility as a valuable aid for decision-makers, especially in the domain of pontoon bridge set selection. 10-19-2023 <![CDATA[ <p>In military operations, the proficient overcoming of water barriers is paramount, with sub-optimal execution potentially leading to significant human and equipment casualties. In this context, global armed forces accord considerable emphasis to the selection of appropriate mechanisms for water obstacle overcoming. This study elucidates the adoption of a Multi-Criteria Decision-Making (MCDM) approach for the selection of optimal pontoon bridge sets for military applications. Criteria identification was undertaken by seven distinguished experts, leading to the determination of weight coefficients using the Defining Interrelationships Between Ranked criteria II (DIBR II) method. Expert assessments were subsequently aggregated utilizing the Normalized Weighted Bonferroni Mean (NWBM) operator. The Multi-Attributive Ideal-Real Comparative Analysis (MAIRCA) method, operationalized within the Fermatean Fuzzy (FF) environment, was harnessed for the discernment of the best alternative. An analysis of the sensitivity of the study's findings with respect to variations in criteria weighting, coupled with a comparative exploration, led to the inference that the proposed MCDM model boasts stability. However, it was noted that the model exhibits sensitivity to shifts in criteria weight coefficients, underscoring its utility as a valuable aid for decision-makers, especially in the domain of pontoon bridge set selection.</p> ]]> A Multi-Criteria Decision-Making Model for Pontoon Bridge Selection: An Application of the DIBR II-NWBM-FF MAIRCA Approach duško tešić darko božanić aleksandar milić doi: 10.56578/jemse020403 Journal of Engineering Management and Systems Engineering 10-19-2023 Journal of Engineering Management and Systems Engineering 10-19-2023 2023 2 4 Article 212 10.56578/jemse020403 https://www.acadlore.com/article/JEMSE/2023_2_4/jemse020403 https://www.acadlore.com/article/JEMSE/2023_2_4/jemse020402 Road transport emerges as a crucial segment of the transportation system, demanding comprehensive analyses of operational performance across passenger and freight domains. This investigation delineates a meticulous multi-criteria analysis of Serbian passenger and freight road transport, relying on data extracted from the Annual Statistical Reports promulgated by the Statistical Office of the Republic of Serbia during 2015-2021. Initially, a compendium of eight pertinent criteria, namely carrying capacity, total number of passenger and tonne-kilometres, employee count, generating power, fuel consumption, and foreign currency receipts, is identified, with a subsequent emphasis placed on six criteria necessitating multi-criteria analysis, applicable cohesively to both passenger and freight transport sectors. Weighting coefficients for each criterion are calculated employing the entropy method, while a multi-criteria ranking of the operational performance of road transport is devised through the application of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. The quintessence of this research lies in the execution of a novel multi-criteria analysis with an aspiration to architect a hierarchy regarding the operational performance within the scrutinised timeframe of road transport in Serbia. 10-10-2023 <![CDATA[ <p>Road transport emerges as a crucial segment of the transportation system, demanding comprehensive analyses of operational performance across passenger and freight domains. This investigation delineates a meticulous multi-criteria analysis of Serbian passenger and freight road transport, relying on data extracted from the Annual Statistical Reports promulgated by the Statistical Office of the Republic of Serbia during 2015-2021. Initially, a compendium of eight pertinent criteria, namely carrying capacity, total number of passenger and tonne-kilometres, employee count, generating power, fuel consumption, and foreign currency receipts, is identified, with a subsequent emphasis placed on six criteria necessitating multi-criteria analysis, applicable cohesively to both passenger and freight transport sectors. Weighting coefficients for each criterion are calculated employing the entropy method, while a multi-criteria ranking of the operational performance of road transport is devised through the application of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. The quintessence of this research lies in the execution of a novel multi-criteria analysis with an aspiration to architect a hierarchy regarding the operational performance within the scrutinised timeframe of road transport in Serbia.</p> ]]> Evaluating the Annual Operational Efficiency of Passenger and Freight Road Transport in Serbia Through Entropy and TOPSIS Methods nikola petrović tanja živojinović jelena mihajlović doi: 10.56578/jemse020402 Journal of Engineering Management and Systems Engineering 10-10-2023 Journal of Engineering Management and Systems Engineering 10-10-2023 2023 2 4 Article 204 10.56578/jemse020402 https://www.acadlore.com/article/JEMSE/2023_2_4/jemse020402 https://www.acadlore.com/article/JEMSE/2023_2_4/jemse020401 Innovative lightweight smart structures incorporating piezoelectric material-based active elements, both as sensors and actuators, have been identified to present manifold advantages over traditional passive systems. Such structures have become intrinsically integrated into smart mechatronic systems, necessitating advanced design, testing, and control techniques. Real-time simulation of shell-type deformable objects, especially when employing the finite element method for non-linear analysis and control, has been challenging due to the extensive computational demand. Presented herein is an efficacious implementation leveraging machine learning with the isogeometric finite element formulation. This implementation focuses on shell-like smart mechatronic structures crafted from composite laminates comprising piezoelectric layers, which are characterised by electro-mechanical coupling. The foundation for the shell kinematics is derived from the Mindlin-Reissner assumptions, effectively incorporating transverse shear effects. While the inclusion of machine learning facilitates real-time efficient operations, the isogeometric finite element analysis (FEA) introduces pronounced advantages over conventional finite element method (FEM), also serving as a valuable source of offline data crucial for the training phases of machine learning algorithms. A piezo-laminated semicircular arch has been analysed to exemplify the effectiveness and performance of the presented methodology. Explorations into further machine learning techniques and intelligent control schemes are also contemplated. 09-27-2023 <![CDATA[ <p>Innovative lightweight smart structures incorporating piezoelectric material-based active elements, both as sensors and actuators, have been identified to present manifold advantages over traditional passive systems. Such structures have become intrinsically integrated into smart mechatronic systems, necessitating advanced design, testing, and control techniques. Real-time simulation of shell-type deformable objects, especially when employing the finite element method for non-linear analysis and control, has been challenging due to the extensive computational demand. Presented herein is an efficacious implementation leveraging machine learning with the isogeometric finite element formulation. This implementation focuses on shell-like smart mechatronic structures crafted from composite laminates comprising piezoelectric layers, which are characterised by electro-mechanical coupling. The foundation for the shell kinematics is derived from the Mindlin-Reissner assumptions, effectively incorporating transverse shear effects. While the inclusion of machine learning facilitates real-time efficient operations, the isogeometric finite element analysis (FEA) introduces pronounced advantages over conventional finite element method (FEM), also serving as a valuable source of offline data crucial for the training phases of machine learning algorithms. A piezo-laminated semicircular arch has been analysed to exemplify the effectiveness and performance of the presented methodology. Explorations into further machine learning techniques and intelligent control schemes are also contemplated.</p> ]]> Isogeometric Finite Element Analysis with Machine Learning Integration for Piezoelectric Laminated Shells žarko ćojbašić nikola ivačko dragan marinković predrag milić goran petrović maša milošević nemanja marković doi: 10.56578/jemse020401 Journal of Engineering Management and Systems Engineering 09-27-2023 Journal of Engineering Management and Systems Engineering 09-27-2023 2023 2 4 Article 196 10.56578/jemse020401 https://www.acadlore.com/article/JEMSE/2023_2_4/jemse020401 https://www.acadlore.com/article/JEMSE/2023_2_3/jemse020305 Effective risk management remains pivotal to the success of any project, especially in the oil and gas industry. This study seeks to identify and quantify the potential risks in Oil and Gas Construction Projects (OGCP) within Pakistan. An exhaustive literature review is undertaken to elucidate various risk classifications and factors. Nine risk classifications emerge from this scrutiny: Health, Safety and Environment (HSE), political, legal, regulatory and bureaucratic, labor and human resources, logistics, economic and financial, technological and technical, and security and management. The novelty of this research lies in the adoption of a quantitative approach, a questionnaire rooted in Failure Modes and Effects Analysis (FMEA), asking respondents to quantify risk factors based on severity, occurrence, and detection. The results obtained from the modified FMEA questionnaire indicate that the highest average risks are associated with logistics, health, environment and safety, and legal, regulatory and bureaucratic factors. Meanwhile, political, human resource, management, and technical and technological factors register as the second-highest risks. Security risk records the least average Risk Priority Number (RPN). The most significant risk factors identified include the lack of a disaster management system, depletion of hydrocarbon resources, corruption, contractual breaches, delays in customs clearance, logistic provider complications, design flaws, technical limitations, and contractor incompetence. This research endeavors to provide academia and industry with expansive knowledge related to the risks inherent in these complex projects. 09-04-2023 <![CDATA[ <p>Effective risk management remains pivotal to the success of any project, especially in the oil and gas industry. This study seeks to identify and quantify the potential risks in Oil and Gas Construction Projects (OGCP) within Pakistan. An exhaustive literature review is undertaken to elucidate various risk classifications and factors. Nine risk classifications emerge from this scrutiny: Health, Safety and Environment (HSE), political, legal, regulatory and bureaucratic, labor and human resources, logistics, economic and financial, technological and technical, and security and management. The novelty of this research lies in the adoption of a quantitative approach, a questionnaire rooted in Failure Modes and Effects Analysis (FMEA), asking respondents to quantify risk factors based on severity, occurrence, and detection. The results obtained from the modified FMEA questionnaire indicate that the highest average risks are associated with logistics, health, environment and safety, and legal, regulatory and bureaucratic factors. Meanwhile, political, human resource, management, and technical and technological factors register as the second-highest risks. Security risk records the least average Risk Priority Number (RPN). The most significant risk factors identified include the lack of a disaster management system, depletion of hydrocarbon resources, corruption, contractual breaches, delays in customs clearance, logistic provider complications, design flaws, technical limitations, and contractor incompetence. This research endeavors to provide academia and industry with expansive knowledge related to the risks inherent in these complex projects.</p> ]]> An Assessment of Risks in Oil and Gas Construction Projects in Pakistan: A Quantitative Approach Using Failure Modes and Effects Analysis osama durrani qasim zeeshan doi: 10.56578/jemse020305 Journal of Engineering Management and Systems Engineering 09-04-2023 Journal of Engineering Management and Systems Engineering 09-04-2023 2023 2 3 Article 180 10.56578/jemse020305 https://www.acadlore.com/article/JEMSE/2023_2_3/jemse020305 https://www.acadlore.com/article/JEMSE/2023_2_3/jemse020304 Project delays pose a substantial challenge in the construction sector. The primary objective of this research is to discern the root causes of project delays in the construction industry and proffer potential solutions, inclusive of the application of building information modeling (BIM) and integrated project delivery (IPD). The integration of IPD and BIM, predicated upon established blueprints, was explored to streamline cost management processes and investigate the potential incorporation of design information within the framework of building price lists. A comprehensive review of extant literature identified 20 possible causes of delays in Iranian construction projects. This study employed a descriptive research design, analyzing data collected from 90 questionnaires completed by construction experts using the statistical package for the social sciences (SPSS) statistical software. A case study of the Dehloran Azad University building project was undertaken, utilizing Revit software for simulation exercises. Field investigations, coupled with a questionnaire disseminated among construction consultants and contractors, elucidated four primary factors contributing to project delays in Iran: 1) the employer's failure to fulfill financial obligations; 2) disregard for the socio-political-economic conditions; 3) absence of a feasibility study prior to tender participation; and 4) inadequate interdepartmental communication. Successful project execution hinges on active team participation and the value that such teamwork brings. The implementation of the IPD model was found to encourage increased enthusiasm and participation. Given that the most significant source of delays in Iran's construction projects was identified as financial issues, the adoption of BIM/IPD may mitigate delays and risks associated with inaccurate estimates. This approach was also found to be effective in projects that are in mid-stage completion. 08-31-2023 <![CDATA[ <p>Project delays pose a substantial challenge in the construction sector. The primary objective of this research is to discern the root causes of project delays in the construction industry and proffer potential solutions, inclusive of the application of building information modeling (BIM) and integrated project delivery (IPD). The integration of IPD and BIM, predicated upon established blueprints, was explored to streamline cost management processes and investigate the potential incorporation of design information within the framework of building price lists. A comprehensive review of extant literature identified 20 possible causes of delays in Iranian construction projects. This study employed a descriptive research design, analyzing data collected from 90 questionnaires completed by construction experts using the statistical package for the social sciences (SPSS) statistical software. A case study of the Dehloran Azad University building project was undertaken, utilizing Revit software for simulation exercises. Field investigations, coupled with a questionnaire disseminated among construction consultants and contractors, elucidated four primary factors contributing to project delays in Iran: 1) the employer's failure to fulfill financial obligations; 2) disregard for the socio-political-economic conditions; 3) absence of a feasibility study prior to tender participation; and 4) inadequate interdepartmental communication. Successful project execution hinges on active team participation and the value that such teamwork brings. The implementation of the IPD model was found to encourage increased enthusiasm and participation. Given that the most significant source of delays in Iran's construction projects was identified as financial issues, the adoption of BIM/IPD may mitigate delays and risks associated with inaccurate estimates. This approach was also found to be effective in projects that are in mid-stage completion.</p> ]]> Mitigating Construction Delays in Iran: An Empirical Evaluation of Building Information Modeling and Integrated Project Delivery milad ghanbari dina zolfaghari zohreh yadegari doi: 10.56578/jemse020304 Journal of Engineering Management and Systems Engineering 08-31-2023 Journal of Engineering Management and Systems Engineering 08-31-2023 2023 2 3 Article 170 10.56578/jemse020304 https://www.acadlore.com/article/JEMSE/2023_2_3/jemse020304 https://www.acadlore.com/article/JEMSE/2023_2_3/jemse020303 Wood, notably in the forms of sawn lumber and glued laminated (glulam) timber, serves as a prevalent structural material for lightweight constructions and bridges with short spans. Over time, timber structures might experience deterioration due to factors such as biological attack, ageing, and escalated service loads. In such cases, reinforcing or repairing the compromised timber components can often be more economical than full replacement. Fiber-reinforced polymer (FRP) composites, particularly those strengthened using carbon fiber, present significant potential in enhancing the stiffness or load-carrying capacity of these timber systems. In the present investigation, the bending behavior of both solid and glulam beams, reinforced with carbon FRP composites in a "U" shape at the bottom layer, was studied experimentally and numerically. It was observed that reinforced glulam beams exhibit superior load-carrying capacity, displacement, modulus of rupture, and modulus of elasticity as compared to their unreinforced solid beam counterparts. Even though both types of beams are fabricated from identical materials, the laminated beams demonstrated markedly enhanced bending characteristics. Moreover, the addition of reinforcement to glulam beams showed a substantial improvement in bending performance. Consistency between numerical simulations, conducted using a finite element analysis program, and experimental outcomes was noted. This research suggests that timber materials, when strengthened with fiber-augmented polymer fabrics, can be accurately represented using numerical tools. 08-31-2023 <![CDATA[ <p>Wood, notably in the forms of sawn lumber and glued laminated (glulam) timber, serves as a prevalent structural material for lightweight constructions and bridges with short spans. Over time, timber structures might experience deterioration due to factors such as biological attack, ageing, and escalated service loads. In such cases, reinforcing or repairing the compromised timber components can often be more economical than full replacement. Fiber-reinforced polymer (FRP) composites, particularly those strengthened using carbon fiber, present significant potential in enhancing the stiffness or load-carrying capacity of these timber systems. In the present investigation, the bending behavior of both solid and glulam beams, reinforced with carbon FRP composites in a "U" shape at the bottom layer, was studied experimentally and numerically. It was observed that reinforced glulam beams exhibit superior load-carrying capacity, displacement, modulus of rupture, and modulus of elasticity as compared to their unreinforced solid beam counterparts. Even though both types of beams are fabricated from identical materials, the laminated beams demonstrated markedly enhanced bending characteristics. Moreover, the addition of reinforcement to glulam beams showed a substantial improvement in bending performance. Consistency between numerical simulations, conducted using a finite element analysis program, and experimental outcomes was noted. This research suggests that timber materials, when strengthened with fiber-augmented polymer fabrics, can be accurately represented using numerical tools.</p> ]]> Numerical and Experimental Evaluation of the Mechanical Behavior of FRP-Strengthened Solid and Glulam Timber Beams şemsettin kilinçarslan yasemin şimşek türker mehmet avcar doi: 10.56578/jemse020303 Journal of Engineering Management and Systems Engineering 08-31-2023 Journal of Engineering Management and Systems Engineering 08-31-2023 2023 2 3 Article 158 10.56578/jemse020303 https://www.acadlore.com/article/JEMSE/2023_2_3/jemse020303 https://www.acadlore.com/article/JEMSE/2023_2_3/jemse020302 In this study, the precision of anatomical models and surgical guides pertaining to the knee joint, fabricated using the mSLA technique, was critically examined. The ITK-SNAP program was employed for the segmentation and reconstruction of knee joint anatomical structures, while surgical guide modelling was executed using the Siemens NX program. Subsequent fabrication of the models was accomplished with the Anycubic Photon Mono 4K MSLA 3D printer. An MCA II articulated arm equipped with a laser head, in conjunction with a TalyScan 150 profilometer, was utilized to gauge both the geometrical fidelity and surface roughness of the resulting models. Results indicated that the geometrical precision of these models remained within a tolerance of +/-0.3 mm. With regard to surface roughness, the Sa parameter was observed to lie between 2 and 2.5 µm. 08-31-2023 <![CDATA[ <p>In this study, the precision of anatomical models and surgical guides pertaining to the knee joint, fabricated using the mSLA technique, was critically examined. The ITK-SNAP program was employed for the segmentation and reconstruction of knee joint anatomical structures, while surgical guide modelling was executed using the Siemens NX program. Subsequent fabrication of the models was accomplished with the Anycubic Photon Mono 4K MSLA 3D printer. An MCA II articulated arm equipped with a laser head, in conjunction with a TalyScan 150 profilometer, was utilized to gauge both the geometrical fidelity and surface roughness of the resulting models. Results indicated that the geometrical precision of these models remained within a tolerance of +/-0.3 mm. With regard to surface roughness, the Sa parameter was observed to lie between 2 and 2.5 µm.</p> ]]> Geometrical Precision and Surface Topography of mSLA-Produced Surgical Guides for the Knee Joint paweł turek jakub jakubiec doi: 10.56578/jemse020302 Journal of Engineering Management and Systems Engineering 08-31-2023 Journal of Engineering Management and Systems Engineering 08-31-2023 2023 2 3 Article 150 10.56578/jemse020302 https://www.acadlore.com/article/JEMSE/2023_2_3/jemse020302 https://www.acadlore.com/article/JEMSE/2023_2_3/jemse020301 In modern mobile machines, mechatronic systems have been integrated, enabling: a) the automation and robotization of machine tasks, b) the regulation of drive system parameters, and c) the transfer and processing of signals pertaining to machine management and monitoring. This study presents an in-depth analysis of mechatronic systems responsible for drive system regulation, transmission automation, and robotization of mobile machine manipulators. Criteria and objectives for regulation and automation are delineated, based on which application software has been developed. Through these mechatronic systems, efficient, ergonomic, and ecologically sound operations of mobile machines are facilitated. 08-31-2023 <![CDATA[ <p style="text-align: justify">In modern mobile machines, mechatronic systems have been integrated, enabling: a) the automation and robotization of machine tasks, b) the regulation of drive system parameters, and c) the transfer and processing of signals pertaining to machine management and monitoring. This study presents an in-depth analysis of mechatronic systems responsible for drive system regulation, transmission automation, and robotization of mobile machine manipulators. Criteria and objectives for regulation and automation are delineated, based on which application software has been developed. Through these mechatronic systems, efficient, ergonomic, and ecologically sound operations of mobile machines are facilitated.</p> ]]> Analysis and Regulation of Mechatronic Systems in Advanced Mobile Machines vesna jovanović dragoslav janošević doi: 10.56578/jemse020301 Journal of Engineering Management and Systems Engineering 08-31-2023 Journal of Engineering Management and Systems Engineering 08-31-2023 2023 2 3 Article 140 10.56578/jemse020301 https://www.acadlore.com/article/JEMSE/2023_2_3/jemse020301 https://www.acadlore.com/article/JEMSE/2023_2_2/jemse020205 In this study, an intelligent optimization system for laser micro-machining operations is developed, utilizing an Adaptive Neuro-Fuzzy Inference System (ANFIS). The heuristic optimization tool, ANFIS, synergistically combines back-propagation training with gradient descent in a unidirectional manner. A comprehensive training set, incorporating experimental data from the literature, highlights the sensitivity of groove depth and recast layer height to specific critical operating factors during the laser micro-machining process. By optimizing lamp current, pulse width, and frequency, the proposed system aims to achieve superior groove depth and recast layer height outcomes. This novel microscopic research holds the potential to captivate both academic scholars and industry professionals. 06-20-2023 <![CDATA[ <p>In this study, an intelligent optimization system for laser micro-machining operations is developed, utilizing an Adaptive Neuro-Fuzzy Inference System (ANFIS). The heuristic optimization tool, ANFIS, synergistically combines back-propagation training with gradient descent in a unidirectional manner. A comprehensive training set, incorporating experimental data from the literature, highlights the sensitivity of groove depth and recast layer height to specific critical operating factors during the laser micro-machining process. By optimizing lamp current, pulse width, and frequency, the proposed system aims to achieve superior groove depth and recast layer height outcomes. This novel microscopic research holds the potential to captivate both academic scholars and industry professionals.</p> ]]> Adaptive Neuro-Fuzzy Optimization for Enhanced Precision in Laser Micro-Machining Operations sanjin troha miloš milovančević aleksandar dimov doi: 10.56578/jemse020205 Journal of Engineering Management and Systems Engineering 06-20-2023 Journal of Engineering Management and Systems Engineering 06-20-2023 2023 2 2 Article 134 10.56578/jemse020205 https://www.acadlore.com/article/JEMSE/2023_2_2/jemse020205 https://www.acadlore.com/article/JEMSE/2023_2_2/jemse020204 The COVID-19 pandemic emerged over three years ago as a public health crisis, swiftly evolving into a worldwide economic crisis with far-reaching implications for global business enterprises and industries. The unprecedented disruption has varied across sectors, with some experiencing severe consequences while others have thrived. As governments and economies continue to recover from the pandemic's effects, it is crucial to analyze and comprehend these impacts to foster sustainable growth and prepare for future disruptions. This study aims to examine the COVID-19 pandemic's ramifications on Pakistan's software business enterprises by addressing three exploratory research questions: a) the pandemic's influence on Pakistan's software business enterprises, b) actions and initiatives undertaken by these enterprises during the pandemic, and c) the contributions made by these enterprises in combating the pandemic. Employing a mixed-methods approach, a survey research design was developed, incorporating both quantitative and qualitative methods to create a questionnaire grounded in a literature review. The findings of the survey are presented and discussed in-depth. This research contributes to the expanding body of knowledge on the COVID-19 pandemic's effects on business enterprises and industries. 06-20-2023 <![CDATA[ <p>The COVID-19 pandemic emerged over three years ago as a public health crisis, swiftly evolving into a worldwide economic crisis with far-reaching implications for global business enterprises and industries. The unprecedented disruption has varied across sectors, with some experiencing severe consequences while others have thrived. As governments and economies continue to recover from the pandemic's effects, it is crucial to analyze and comprehend these impacts to foster sustainable growth and prepare for future disruptions. This study aims to examine the COVID-19 pandemic's ramifications on Pakistan's software business enterprises by addressing three exploratory research questions: a) the pandemic's influence on Pakistan's software business enterprises, b) actions and initiatives undertaken by these enterprises during the pandemic, and c) the contributions made by these enterprises in combating the pandemic. Employing a mixed-methods approach, a survey research design was developed, incorporating both quantitative and qualitative methods to create a questionnaire grounded in a literature review. The findings of the survey are presented and discussed in-depth. This research contributes to the expanding body of knowledge on the COVID-19 pandemic's effects on business enterprises and industries.</p> ]]> The Impact of the COVID-19 Pandemic on Software Business Enterprises in Pakistan: Analysis and Implications sundus haroon qasim zeeshan doi: 10.56578/jemse020204 Journal of Engineering Management and Systems Engineering 06-20-2023 Journal of Engineering Management and Systems Engineering 06-20-2023 2023 2 2 Article 123 10.56578/jemse020204 https://www.acadlore.com/article/JEMSE/2023_2_2/jemse020204 https://www.acadlore.com/article/JEMSE/2023_2_2/jemse020203 With the expansion of steel production via electric arc furnaces, an increase in dust generation—a by-product of these operations—poses substantial challenges. These difficulties stem from land use restrictions for large-scale dust waste storage and the environmental implications of heavy metal contamination inherent in the dust. In an effort to promote the repurposing of this potentially hazardous solid waste, this study examines the concentration and leachability of various heavy metals in this dust. Digestion of the dust samples was carried out in a controlled laboratory setting, after which the concentrations of iron (Fe), magnesium (Mg), zinc (Zn), manganese (Mn), nickel (Ni), lead (Pb), cadmium (Cd), and cobalt (Co) were determined using flame atomic absorption spectrometry. The mean concentrations of these heavy metals in the dust were found to be in the following descending order (in mg/kg): Fe> Mg> Zn> Mn> Ni> Pb> Cu> Cd> Co. Water leaching tests were subsequently conducted, revealing that Co and Cd exhibited the greatest leachability at varying pH levels. Conversely, Fe and Ni displayed minimal leachability. These findings have significant implications for the reuse and environmental management of electric arc furnace dust. 06-20-2023 <![CDATA[ <p>With the expansion of steel production via electric arc furnaces, an increase in dust generation—a by-product of these operations—poses substantial challenges. These difficulties stem from land use restrictions for large-scale dust waste storage and the environmental implications of heavy metal contamination inherent in the dust. In an effort to promote the repurposing of this potentially hazardous solid waste, this study examines the concentration and leachability of various heavy metals in this dust. Digestion of the dust samples was carried out in a controlled laboratory setting, after which the concentrations of iron (Fe), magnesium (Mg), zinc (Zn), manganese (Mn), nickel (Ni), lead (Pb), cadmium (Cd), and cobalt (Co) were determined using flame atomic absorption spectrometry. The mean concentrations of these heavy metals in the dust were found to be in the following descending order (in mg/kg): Fe> Mg> Zn> Mn> Ni> Pb> Cu> Cd> Co. Water leaching tests were subsequently conducted, revealing that Co and Cd exhibited the greatest leachability at varying pH levels. Conversely, Fe and Ni displayed minimal leachability. These findings have significant implications for the reuse and environmental management of electric arc furnace dust.</p> ]]> Evaluating the Concentration and Leachability of Heavy Metals in Electric Arc Furnace Dust: Implications for Environmental Management mehdi saadati zeinab hosseinzadeh abbas ali zamani ehsan ashabi doi: 10.56578/jemse020203 Journal of Engineering Management and Systems Engineering 06-20-2023 Journal of Engineering Management and Systems Engineering 06-20-2023 2023 2 2 Article 117 10.56578/jemse020203 https://www.acadlore.com/article/JEMSE/2023_2_2/jemse020203 https://www.acadlore.com/article/JEMSE/2023_2_2/jemse020202 This study focuses on the detailed reconstruction of chain wheel geometry utilizing measurement data gathered from the MarSurfXC20 contact system and the iNEXIVE VMA 2520 optical system. Supplementary data were also gathered from a digital micrometer and a caliper to provide a comprehensive data set for the analyzed geometry. The geometric model of the chain wheel was then constructed using Siemens NX software. The reconstructed model was subsequently compared with the original design specifications to assess the fidelity of the reconstructed model. Results demonstrate a high degree of correlation between the model generated by reverse engineering and the original design model. Despite the satisfactory correlation, potential inaccuracies were identified, necessitating further research to mitigate these discrepancies and optimize the procedures for parameters beyond the established tolerance. The study affirms the feasibility of utilizing contact and optical measuring systems in the reverse engineering process of chain wheel geometry, although it underscores the need for additional refinement to improve the model's accuracy. 06-20-2023 <![CDATA[ <p>This study focuses on the detailed reconstruction of chain wheel geometry utilizing measurement data gathered from the MarSurfXC20 contact system and the iNEXIVE VMA 2520 optical system. Supplementary data were also gathered from a digital micrometer and a caliper to provide a comprehensive data set for the analyzed geometry. The geometric model of the chain wheel was then constructed using Siemens NX software. The reconstructed model was subsequently compared with the original design specifications to assess the fidelity of the reconstructed model. Results demonstrate a high degree of correlation between the model generated by reverse engineering and the original design model. Despite the satisfactory correlation, potential inaccuracies were identified, necessitating further research to mitigate these discrepancies and optimize the procedures for parameters beyond the established tolerance. The study affirms the feasibility of utilizing contact and optical measuring systems in the reverse engineering process of chain wheel geometry, although it underscores the need for additional refinement to improve the model's accuracy.</p> ]]> Precision Analysis of Chain Wheel Geometry Reconstruction Based on Contact and Optical Measurement Data paweł turek jakub jędras doi: 10.56578/jemse020202 Journal of Engineering Management and Systems Engineering 06-20-2023 Journal of Engineering Management and Systems Engineering 06-20-2023 2023 2 2 Article 108 10.56578/jemse020202 https://www.acadlore.com/article/JEMSE/2023_2_2/jemse020202 https://www.acadlore.com/article/JEMSE/2023_2_2/jemse020201 This investigation elucidates the influence of Administrative Empowerment (AEM) on employee performance within the distribution sector of petroleum products in Torbat Heydarieh, Iran, utilizing a case-study approach to examine the correlational effects of varied AEM factors. A descriptive-analytical methodology was employed, with data collected through a standardized empowerment questionnaire, administered to the entire workforce as the population of interest. The validity of the questionnaire was ensured through the application of the Kolmogorov-Smirnov (K-S) test and Cronbach's alpha, while regression correlation coefficients were used to confirm the legitimacy of the resultant data. A simple random sampling method was employed, yielding a sample size of 45 participants. The principal outcome of this research suggests a consensus regarding the positive influence of AEM on expertise-based outcomes within the Iranian petroleum product distribution sector. Further, the study identified the workplace environment, morale, organizational belongingness, access to knowledge information, and job skills as the most potent determinants influencing human resource motivation. These elements surfaced as critical, feasible, and interesting aspects of work, and were found to be of paramount importance in the empowerment process. 06-20-2023 <![CDATA[ <p>This investigation elucidates the influence of Administrative Empowerment (AEM) on employee performance within the distribution sector of petroleum products in Torbat Heydarieh, Iran, utilizing a case-study approach to examine the correlational effects of varied AEM factors. A descriptive-analytical methodology was employed, with data collected through a standardized empowerment questionnaire, administered to the entire workforce as the population of interest. The validity of the questionnaire was ensured through the application of the Kolmogorov-Smirnov (K-S) test and Cronbach's alpha, while regression correlation coefficients were used to confirm the legitimacy of the resultant data. A simple random sampling method was employed, yielding a sample size of 45 participants. The principal outcome of this research suggests a consensus regarding the positive influence of AEM on expertise-based outcomes within the Iranian petroleum product distribution sector. Further, the study identified the workplace environment, morale, organizational belongingness, access to knowledge information, and job skills as the most potent determinants influencing human resource motivation. These elements surfaced as critical, feasible, and interesting aspects of work, and were found to be of paramount importance in the empowerment process.</p> ]]> Examining the Role of Empowerment Criteria on Employee Performance: A Quantitative Analysis in the Oil Industry mohammad reza gharib najmeh jamali sajjad nikkhah chamanabad masoud goharimanesh doi: 10.56578/jemse020201 Journal of Engineering Management and Systems Engineering 06-20-2023 Journal of Engineering Management and Systems Engineering 06-20-2023 2023 2 2 Article 96 10.56578/jemse020201 https://www.acadlore.com/article/JEMSE/2023_2_2/jemse020201 https://www.acadlore.com/article/JEMSE/2023_2_1/jemse020105 Aircraft maintenance is an expansive multidisciplinary field which entails robust design and optimization of extensive maintenance operations and procedures; encompassing the fault identification, detection and rectification, and overhauling, repair or modification of aircraft systems, subsystems, and components, as well as the scheduling for various maintenance operations, in compliance with the aviation standards; in order to predict, pre-empt and prevent failures and thus ensure the continual reliability of aircraft. Advances in Big Data Analytics (BDA) and artificial intelligence techniques have revolutionized predictive maintenance operations. Predictive maintenance is making big strides in the aerospace sector accompanied by a variety of prognostic health management options. Artificial intelligence algorithms have recently been extensively applied to optimize aircraft maintenance systems and operations. Several researchers have proposed, analysed, and investigated the applications of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) based data analytics for predictive maintenance of aircraft systems, subsystems, and components. This paper provides a comprehensive review of the ML techniques like Multilayer Perceptron (MLP), Logic Regression (LR), Random Forest (RF), Artificial Neural Network (ANN), Support Vector Regression (SVR), Linear Regression (LR), and other common ML techniques for their present implementation and potential future applications in aircraft maintenance. 03-29-2023 <![CDATA[ <p>Aircraft maintenance is an expansive multidisciplinary field which entails robust design and optimization of extensive maintenance operations and procedures; encompassing the fault identification, detection and rectification, and overhauling, repair or modification of aircraft systems, subsystems, and components, as well as the scheduling for various maintenance operations, in compliance with the aviation standards; in order to predict, pre-empt and prevent failures and thus ensure the continual reliability of aircraft. Advances in Big Data Analytics (BDA) and artificial intelligence techniques have revolutionized predictive maintenance operations. Predictive maintenance is making big strides in the aerospace sector accompanied by a variety of prognostic health management options. Artificial intelligence algorithms have recently been extensively applied to optimize aircraft maintenance systems and operations. Several researchers have proposed, analysed, and investigated the applications of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) based data analytics for predictive maintenance of aircraft systems, subsystems, and components. This paper provides a comprehensive review of the ML techniques like Multilayer Perceptron (MLP), Logic Regression (LR), Random Forest (RF), Artificial Neural Network (ANN), Support Vector Regression (SVR), Linear Regression (LR), and other common ML techniques for their present implementation and potential future applications in aircraft maintenance.</p> ]]> Applications of Machine Learning in Aircraft Maintenance umur karaoğlu osinachi mbah qasim zeeshan doi: 10.56578/jemse020105 Journal of Engineering Management and Systems Engineering 03-29-2023 Journal of Engineering Management and Systems Engineering 03-29-2023 2023 2 1 Article 76 10.56578/jemse020105 https://www.acadlore.com/article/JEMSE/2023_2_1/jemse020105 https://www.acadlore.com/article/JEMSE/2023_2_1/jemse020104 In the global economy, plastics are considered a versatile and ubiquitous material. It can reach to marine ecosystems through diverse channels, such as road runoff, wastewater pathways, and improper waste management. Therefore, rapid mitigation and reduction are required for this ever-growing problem. The marine habitats are believed to be the highest emitters and absorbers of O2 and CO2 respectively. As such, every day, the prominence of managing the litter in the ocean is growing effectively and efficiently. One of the most significant challenges in oceanography is creating a comprehensive meshless algorithm to handle the mathematical representation of waste plastic management in the ocean. This research dedicates to studying the dynamics of waste plastic management model governed by a mathematical representation depending on three components viz. Waste plastic (W), Marine litter (M) and Recycling of debris (R), i.e., WMR model. In this regard, an unsupervised machine learning approach, namely Mexican Hat Wavelet Neural Network (MhWNN) refined by the efficient Limited-memory Broyden–Fletcher–Goldfarb–Shanno algorithm (L-BFGS), i.e., MhWNN-LBFGS model has been implemented for handling the non-linear phenomena of WMR models. Besides, the obtained solution is meshfree and compared with the state-of-art numerical result to establish the precision of the MhWNN-LBFGS model. Furthermore, different global statistical measures (MAPE, TIC, RMSE, and ENSE) have been computed at twenty testing points to validate the stability of the proposed algorithm. 03-29-2023 <![CDATA[ <p>In the global economy, plastics are considered a versatile and ubiquitous material. It can reach to marine ecosystems through diverse channels, such as road runoff, wastewater pathways, and improper waste management. Therefore, rapid mitigation and reduction are required for this ever-growing problem. The marine habitats are believed to be the highest emitters and absorbers of O₂ and CO₂ respectively. As such, every day, the prominence of managing the litter in the ocean is growing effectively and efficiently. One of the most significant challenges in oceanography is creating a comprehensive meshless algorithm to handle the mathematical representation of waste plastic management in the ocean. This research dedicates to studying the dynamics of waste plastic management model governed by a mathematical representation depending on three components viz. Waste plastic (W), Marine litter (M) and Recycling of debris (R), i.e., WMR model. In this regard, an unsupervised machine learning approach, namely Mexican Hat Wavelet Neural Network (M_hWNN) refined by the efficient Limited-memory Broyden–Fletcher–Goldfarb–Shanno algorithm (L-BFGS), i.e., M_hWNN-LBFGS model has been implemented for handling the non-linear phenomena of WMR models. Besides, the obtained solution is meshfree and compared with the state-of-art numerical result to establish the precision of the M_hWNN-LBFGS model. Furthermore, different global statistical measures (MAPE, TIC, RMSE, and ENSE) have been computed at twenty testing points to validate the stability of the proposed algorithm.</p> ]]> Modeling of Mexican Hat Wavelet Neural Network with L-BFGS Algorithm for Simulating the Recycling Procedure of Waste Plastic in Ocean arup kumar sahoo snehashish chakraverty doi: 10.56578/jemse020104 Journal of Engineering Management and Systems Engineering 03-29-2023 Journal of Engineering Management and Systems Engineering 03-29-2023 2023 2 1 Article 61 10.56578/jemse020104 https://www.acadlore.com/article/JEMSE/2023_2_1/jemse020104 https://www.acadlore.com/article/JEMSE/2023_2_1/jemse020103 Affected by new trends, automobile companies have altered stakeholder requirements on their main product - the automobile. With enactment of new regulations concerning sustainability, new features appeared quickly, such as electrification, sharing services, autonomous mobility and so on. In this study, we present sustainability as a stakeholder and analyze the method of its realization in Systems Engineering (SE) based product development. Formula SAE provides a validated setting to conduct experiments on integrating sustainability into the classical product requirement architectures. By taking into consideration the use of SE or adding other methodological frameworks, findings can establish a new setting in sustainability research. The results of this study may be enlightening for scholars and practitioners and calls for further research on embedding sustainability requirements in automotive product development by using SE. 03-20-2023 <![CDATA[ <p>Affected by new trends, automobile companies have altered stakeholder requirements on their main product - the automobile. With enactment of new regulations concerning sustainability, new features appeared quickly, such as electrification, sharing services, autonomous mobility and so on. In this study, we present sustainability as a stakeholder and analyze the method of its realization in Systems Engineering (SE) based product development. Formula SAE provides a validated setting to conduct experiments on integrating sustainability into the classical product requirement architectures. By taking into consideration the use of SE or adding other methodological frameworks, findings can establish a new setting in sustainability research. The results of this study may be enlightening for scholars and practitioners and calls for further research on embedding sustainability requirements in automotive product development by using SE.</p> ]]> Systems Engineering Based Sustainability Improvement in Automotive Product Development Tamás Kolossváry péter németh doi: 10.56578/jemse020103 Journal of Engineering Management and Systems Engineering 03-20-2023 Journal of Engineering Management and Systems Engineering 03-20-2023 2023 2 1 Article 53 10.56578/jemse020103 https://www.acadlore.com/article/JEMSE/2023_2_1/jemse020103 https://www.acadlore.com/article/JEMSE/2023_2_1/jemse020102 Choosing a battery supplier is a vital decision-making problem, for which it is essential to obtain stable evaluations. For such sustainable supplier evaluation, multi-criteria decision analysis (MCDA) methods are often used, as their ability to handle uncertain data gives experts more significant opportunities to consider a broader range of cases. However, given the great number of MCDA approaches, it is challenging to find out which approach is the most appropriate. Therefore, this paper presents a sensitivity analysis on evaluating battery suppliers by ARAS, EDAS, MAIRCA, TOPSIS, and VIKOR methods in a fuzzy environment. The provided study presented similar results for the considered MCDA methods confirmed by the WS similarity measure of rankings and the weighted Spearman correlation . On the other hand, the sensitivity analysis conducted on the considered methods indicated that the most relevant criteria for this problem are transportation cost, delivery time, and warranty period. 03-08-2023 <![CDATA[ <p>Choosing a battery supplier is a vital decision-making problem, for which it is essential to obtain stable evaluations. For such sustainable supplier evaluation, multi-criteria decision analysis (MCDA) methods are often used, as their ability to handle uncertain data gives experts more significant opportunities to consider a broader range of cases. However, given the great number of MCDA approaches, it is challenging to find out which approach is the most appropriate. Therefore, this paper presents a sensitivity analysis on evaluating battery suppliers by ARAS, EDAS, MAIRCA, TOPSIS, and VIKOR methods in a fuzzy environment. The provided study presented similar results for the considered MCDA methods confirmed by the WS similarity measure of rankings and the weighted Spearman correlation . On the other hand, the sensitivity analysis conducted on the considered methods indicated that the most relevant criteria for this problem are transportation cost, delivery time, and warranty period.</p> ]]> How Do the Criteria Affect Sustainable Supplier Evaluation? - A Case Study Using Multi-Criteria Decision Analysis Methods in a Fuzzy Environment jakub więckowski bartłomiej kizielewicz andrii shekhovtsov wojciech sałabun doi: 10.56578/jemse020102 Journal of Engineering Management and Systems Engineering 03-08-2023 Journal of Engineering Management and Systems Engineering 03-08-2023 2023 2 1 Article 37 10.56578/jemse020102 https://www.acadlore.com/article/JEMSE/2023_2_1/jemse020102 https://www.acadlore.com/article/JEMSE/2023_2_1/jemse020101 Preventive conservation is conductive to the long-term preservation of works of art. In order to realize the avoidance of damages in advance, risk management as well as foresighted thinking is required. The application of the method of engineering mechanics for preventive conservation is at the very beginning of its development. This article is a contribution to this still very young field. Generally, sensitive artworks combine all properties of complex mechanical structures. Oil paintings on canvas, for instance, are asymmetric, multiple curvilinear structures made of aged anisotropic compound materials with cracks and other damages. Due to their popularity, some artworks travel a lot, and during the exhibition and storage, they are always exposed to shocks and vibrations, therefore the protection of sensitive paintings is a demanding task, the solution of which requires the multidisciplinary cooperation especially in the context of engineering mechanics with its many specializations. The subject of the presented research is an artificial aged painting dummy in the simplest conceivable composition. This paper aims to describe the mechanical behavior of this test object, which is the basic requirement for the development of technological protective measures. The concept of the digital twin, known from Industry 4.0, is used to solve this task. This article focuses on the design of a virtual painting dummy that has the same static and dynamic behavior as the investigated real test object. Therefore, the deflection of the real dummy in lying position as well as the curvature of its standing position without and with dynamic excitations have been measured. The advantage of the analytical and Finite Element Analysis (FEA) models presented are their practicability and quick realizability at fair correlation. The concept presented offers a potential way to assess and finally reduce the risk for original paintings during various transport, exhibition, and storage situations with the help of virtual objects. 02-23-2023 <![CDATA[ <p>Preventive conservation is conductive to the long-term preservation of works of art. In order to realize the avoidance of damages in advance, risk management as well as foresighted thinking is required. The application of the method of engineering mechanics for preventive conservation is at the very beginning of its development. This article is a contribution to this still very young field. Generally, sensitive artworks combine all properties of complex mechanical structures. Oil paintings on canvas, for instance, are asymmetric, multiple curvilinear structures made of aged anisotropic compound materials with cracks and other damages. Due to their popularity, some artworks travel a lot, and during the exhibition and storage, they are always exposed to shocks and vibrations, therefore the protection of sensitive paintings is a demanding task, the solution of which requires the multidisciplinary cooperation especially in the context of engineering mechanics with its many specializations. The subject of the presented research is an artificial aged painting dummy in the simplest conceivable composition. This paper aims to describe the mechanical behavior of this test object, which is the basic requirement for the development of technological protective measures. The concept of the digital twin, known from Industry 4.0, is used to solve this task. This article focuses on the design of a virtual painting dummy that has the same static and dynamic behavior as the investigated real test object. Therefore, the deflection of the real dummy in lying position as well as the curvature of its standing position without and with dynamic excitations have been measured. The advantage of the analytical and Finite Element Analysis (FEA) models presented are their practicability and quick realizability at fair correlation. The concept presented offers a potential way to assess and finally reduce the risk for original paintings during various transport, exhibition, and storage situations with the help of virtual objects.</p> ]]> From “How to Model a Painting” to the Digital Twin Design of Canvas Paintings kerstin kracht michael meyer-coors roland schröder doi: 10.56578/jemse020101 Journal of Engineering Management and Systems Engineering 02-23-2023 Journal of Engineering Management and Systems Engineering 02-23-2023 2023 2 1 Article 1 10.56578/jemse020101 https://www.acadlore.com/article/JEMSE/2023_2_1/jemse020101 https://www.acadlore.com/article/JEMSE/2022_1_2/jemse010205 Optimizing the supply chain of agricultural products is an important way to revitalize the rural economy. The perishable products are agricultural products with special properties and great potential. Game theory is an effective research tool for maximizing revenue management in competition and cooperation. Therefore, this study reviews the literature on the pricing and procurement of perishable products in the past three years under the game theory. Firstly, this study summarizes the common game models of supply chain through the review of multi-objective models. Secondly, it summarizes the literature on pricing and purchasing, and explores the strategies of product management, benefit maximization and supply chain coordination. Finally, based on some cutting-edge results, this study proposes promising research directions in the future. This study is helpful to summarize the application of game theory in supply chain management, help rural agricultural products to achieve maximum benefit, and solve the problem of supply and demand optimization. 12-30-2022 <![CDATA[ <p>Optimizing the supply chain of agricultural products is an important way to revitalize the rural economy. The perishable products are agricultural products with special properties and great potential. Game theory is an effective research tool for maximizing revenue management in competition and cooperation. Therefore, this study reviews the literature on the pricing and procurement of perishable products in the past three years under the game theory. Firstly, this study summarizes the common game models of supply chain through the review of multi-objective models. Secondly, it summarizes the literature on pricing and purchasing, and explores the strategies of product management, benefit maximization and supply chain coordination. Finally, based on some cutting-edge results, this study proposes promising research directions in the future. This study is helpful to summarize the application of game theory in supply chain management, help rural agricultural products to achieve maximum benefit, and solve the problem of supply and demand optimization.</p> ]]> Supply and Demand Optimization of Agricultural Products in Game Theory: A State-of-the-Art Review kuo-yi lin li hu doi: 10.56578/jemse010205 Journal of Engineering Management and Systems Engineering 12-30-2022 Journal of Engineering Management and Systems Engineering 12-30-2022 2022 1 2 Article 76 10.56578/jemse010205 https://www.acadlore.com/article/JEMSE/2022_1_2/jemse010205 https://www.acadlore.com/article/JEMSE/2022_1_2/jemse010204 Lightweight is one of the primary design goals for the innovative development of wheel hubs. A new type of automobile wheel rim is proposed in this paper. The wheel rim is divided into wheel rim face and rib. The thickness of the wheel rim surface is studied by parameter optimization. The rib layout is designed based on honeycomb features by considering processing technology and load-bearing performance of wheel hub. A novel wheel rim named “honeycomb wheel rim” is given. The mechanical performances are analyzed and the results show that the weight of new wheel rim was reduced by 12.61%, and the stiffness and strength can meet the design requirements. 12-30-2022 <![CDATA[ <p>Lightweight is one of the primary design goals for the innovative development of wheel hubs. A new type of automobile wheel rim is proposed in this paper. The wheel rim is divided into wheel rim face and rib. The thickness of the wheel rim surface is studied by parameter optimization. The rib layout is designed based on honeycomb features by considering processing technology and load-bearing performance of wheel hub. A novel wheel rim named “honeycomb wheel rim” is given. The mechanical performances are analyzed and the results show that the weight of new wheel rim was reduced by 12.61%, and the stiffness and strength can meet the design requirements.</p> ]]> Innovative Design of Automobile Wheel Rim Based on Honeycomb Features zhaohua wang chaoshi wang guobiao yang fenghe wu doi: 10.56578/jemse010204 Journal of Engineering Management and Systems Engineering 12-30-2022 Journal of Engineering Management and Systems Engineering 12-30-2022 2022 1 2 Article 67 10.56578/jemse010204 https://www.acadlore.com/article/JEMSE/2022_1_2/jemse010204 https://www.acadlore.com/article/JEMSE/2022_1_2/jemse010203 Construction projects are of a particular nature and are affected by many factors, which exposes them to risks due to the long implementation period and the multiplicity of phases from the project idea phase through the implementation phase to the final delivery of the project, which leads to increased uncertainty and increased likelihood of these risks. This paper examines the risks in construction projects in Libya, and their impact on project objectives. This research identified risks in construction projects based on previous studies and a number of interviews with experts in construction projects, as well as field visits to project sites. On this basis, a questionnaire was prepared to locate and identify the risks that construction projects may face and was distributed to a number of local companies affiliated to the Libyan state operating in the construction sector. After the compilation of the questionnaire, the risks were analyzed qualitatively and quantitatively to determine the impact of each risk and the probability of its occurrence. The results of the study showed that 28% of the risks are certain and high, and 53% of the risks affect the project implementation time to a high degree. The results also showed a strong correlation between the probability of occurrence of the risks. Grey theory was used to weigh and rank the most important risks, and the most important of these was the insufficient manpower, material and equipment criterion. 12-30-2022 <![CDATA[ <p>Construction projects are of a particular nature and are affected by many factors, which exposes them to risks due to the long implementation period and the multiplicity of phases from the project idea phase through the implementation phase to the final delivery of the project, which leads to increased uncertainty and increased likelihood of these risks. This paper examines the risks in construction projects in Libya, and their impact on project objectives. This research identified risks in construction projects based on previous studies and a number of interviews with experts in construction projects, as well as field visits to project sites. On this basis, a questionnaire was prepared to locate and identify the risks that construction projects may face and was distributed to a number of local companies affiliated to the Libyan state operating in the construction sector. After the compilation of the questionnaire, the risks were analyzed qualitatively and quantitatively to determine the impact of each risk and the probability of its occurrence. The results of the study showed that 28% of the risks are certain and high, and 53% of the risks affect the project implementation time to a high degree. The results also showed a strong correlation between the probability of occurrence of the risks. Grey theory was used to weigh and rank the most important risks, and the most important of these was the insufficient manpower, material and equipment criterion.</p> ]]> Risk Assessment in Construction Projects Using the Grey Theory ibrahim badi mouhamed bayane bouraima muhammad lawan jibril doi: 10.56578/jemse010203 Journal of Engineering Management and Systems Engineering 12-30-2022 Journal of Engineering Management and Systems Engineering 12-30-2022 2022 1 2 Article 58 10.56578/jemse010203 https://www.acadlore.com/article/JEMSE/2022_1_2/jemse010203 https://www.acadlore.com/article/JEMSE/2022_1_2/jemse010202 Due to the wear of the brake linings of the rear brake, as well as due to the stretching of the steel rope of the handbrake control during the exploitation of the vehicle, the transmission mechanism of the handbrake increases the predefined clearances, that is, the free travel of the control lever increases. This free travel can increase to the extent that it compromises the normal functioning of the braking system. For this reason, all parking brake systems contain backlash adjustment mechanisms. On most vehicles, this adjustment is done manually, which means that the vehicle is periodically taken out of service for servicing. For this reason, the application of various mechanisms for self- adjustment of the handbrake, during the actual exploitation of the vehicle, began. This paper presents the process of design and construction of an innovative mechanism for continuous self-adjustment of the handbrake, without withdrawing the vehicle from operation. 12-30-2022 <![CDATA[ <p><span style="color: rgb(0, 0, 0);">Due to the wear of the brake linings of the rear brake, as well as due to the stretching of the steel rope of the handbrake control during the exploitation of the vehicle, the transmission mechanism of the handbrake increases the predefined clearances, that is, the free travel of the control lever increases. This free travel can increase to the extent that it compromises the normal functioning of the braking system. For this reason, all parking brake systems contain backlash adjustment mechanisms. On most vehicles, this adjustment is done manually, which means that the vehicle is periodically taken out of service for servicing. For this reason, the application of various mechanisms for self- adjustment of the handbrake, during the actual exploitation of the vehicle, began. This paper presents the process of design and construction of an innovative mechanism for continuous self-adjustment of the handbrake, without withdrawing the vehicle from operation.</span></p> ]]> Self-Adjusting Handbrake Mechanism Design ana pavlovic miroslav zivkovic snezana vulovic doi: 10.56578/jemse010202 Journal of Engineering Management and Systems Engineering 12-30-2022 Journal of Engineering Management and Systems Engineering 12-30-2022 2022 1 2 Article 51 10.56578/jemse010202 https://www.acadlore.com/article/JEMSE/2022_1_2/jemse010202 https://www.acadlore.com/article/JEMSE/2022_1_2/jemse010201 Over 700 bike-sharing systems are currently in operation worldwide, and the number of systems has grown quickly in recent years. Rwanda's bike-sharing system has only recently begun operations and has encountered numerous challenges. The current study used an Ordinal Priority Approach (OPA) to examine these challenges and provide an acceptable strategy for overcoming them. Five strategies have been established. These strategies are prioritized using four criteria. The results indicate that “theft” and “damage of some bikes when being returned” are the most critical challenges while the first alternative “improving the current bike infrastructure to better serve the bike share system” is the appropriate strategy to overcome these challenges for a successful operation of the bike share system. Taking into account the findings, recommendations were provided to help local administrative bodies handle these challenges. 12-30-2022 <![CDATA[ <p>Over 700 bike-sharing systems are currently in operation worldwide, and the number of systems has grown quickly in recent years. Rwanda's bike-sharing system has only recently begun operations and has encountered numerous challenges. The current study used an Ordinal Priority Approach (OPA) to examine these challenges and provide an acceptable strategy for overcoming them. Five strategies have been established. These strategies are prioritized using four criteria. The results indicate that “theft” and “damage of some bikes when being returned” are the most critical challenges while the first alternative “improving the current bike infrastructure to better serve the bike share system” is the appropriate strategy to overcome these challenges for a successful operation of the bike share system. Taking into account the findings, recommendations were provided to help local administrative bodies handle these challenges.</p> ]]> Sustainable Strategies for the Successful Operation of the Bike-Sharing System Using an Ordinal Priority Approach clement kiprotich kiptum mouhamed bayane bouraima željko stević sam okemwa sammy birech yanjun qiu doi: 10.56578/jemse010201 Journal of Engineering Management and Systems Engineering 12-30-2022 Journal of Engineering Management and Systems Engineering 12-30-2022 2022 1 2 Article 43 10.56578/jemse010201 https://www.acadlore.com/article/JEMSE/2022_1_2/jemse010201 https://www.acadlore.com/article/JEMSE/2022_1_1/jemse010105 The logistics industry faces many challenges, such as low efficiency and transparency, and data cannot be updated in real time. Digital twin in logistics is regarded as a new technology that can lead the further development of logistics. It can realize the integration of logistics entity and virtual environment in the logistics process to improve transparency and reduce risks. Therefore, in recent years, it has aroused widespread concern, and many researchers have studied the application of digital twins in the field of logistics. However, there are still some problems in the practical application process. This study aims to analyze the current status of digital twin citation in the logistics industry, and comprehensively review the application and limitations of DT with a systematic evaluation method. After careful searching of the database, fourteen related literatures were selected for key classification and analysis. This research shows that Digital Twin has the ability to solve some challenges in the field of logistics. 11-29-2022 <![CDATA[ <p>The logistics industry faces many challenges, such as low efficiency and transparency, and data cannot be updated in real time. Digital twin in logistics is regarded as a new technology that can lead the further development of logistics. It can realize the integration of logistics entity and virtual environment in the logistics process to improve transparency and reduce risks. Therefore, in recent years, it has aroused widespread concern, and many researchers have studied the application of digital twins in the field of logistics. However, there are still some problems in the practical application process. This study aims to analyze the current status of digital twin citation in the logistics industry, and comprehensively review the application and limitations of DT with a systematic evaluation method. After careful searching of the database, fourteen related literatures were selected for key classification and analysis. This research shows that Digital Twin has the ability to solve some challenges in the field of logistics.</p> ]]> A Review of Digital Twin in Logistics: Applications and Future Works kuo-yi lin yuan yao doi: 10.56578/jemse010105 Journal of Engineering Management and Systems Engineering 11-29-2022 Journal of Engineering Management and Systems Engineering 11-29-2022 2022 1 1 Article 32 10.56578/jemse010105 https://www.acadlore.com/article/JEMSE/2022_1_1/jemse010105 https://www.acadlore.com/article/JEMSE/2022_1_1/jemse010104 The production process usually involves several processes that are divided into production lines. The processes in this production line affect the costs incurred by the company. From the analysis results, the costs arising from production line activities are very high. Therefore, the company strives to reduce production costs by paying attention to the aspects that result in the emergence of waste. The method used is by combining the process on the machining line. This study was conducted to find out the effect of combining process lines on production efficiency. The results of this study are expected to be an input in determining production planning in the enterprise. This study didn’t use sampling. From the results of the study, there was an increase in the daily production of gear A (0.86%) and gear B (1.12%). From this merger, the company was able to optimize manpower and cut production WIP storage areas. 11-29-2022 <![CDATA[ <p>The production process usually involves several processes that are divided into production lines. The processes in this production line affect the costs incurred by the company. From the analysis results, the costs arising from production line activities are very high. Therefore, the company strives to reduce production costs by paying attention to the aspects that result in the emergence of waste. The method used is by combining the process on the machining line. This study was conducted to find out the effect of combining process lines on production efficiency. The results of this study are expected to be an input in determining production planning in the enterprise. This study didn’t use sampling. From the results of the study, there was an increase in the daily production of gear A (0.86%) and gear B (1.12%). From this merger, the company was able to optimize manpower and cut production WIP storage areas.</p> ]]> Effect of Combined Processing Lines on Production Efficiency and Productivity fibi eko putra andreamara andreamara doi: 10.56578/jemse010104 Journal of Engineering Management and Systems Engineering 11-29-2022 Journal of Engineering Management and Systems Engineering 11-29-2022 2022 1 1 Article 23 10.56578/jemse010104 https://www.acadlore.com/article/JEMSE/2022_1_1/jemse010104 https://www.acadlore.com/article/JEMSE/2022_1_1/jemse010103 The integrated scheduling of production and maintenance can make equipment maintenance in line with the production pace, so as to effectively prevent anormal interruptions of the production process due to equipment failure, and ensure the smooth implementation of the production scheduling plan. Aiming at the parallel machine job-shop environment, and considering stochastic machine failures and different degradation speeds of parallel machines, this paper introduced the minimal maintenance and preventive maintenance strategies to establish an integrated scheduling model for production and maintenance, designed a genetic algorithm based on process coding and binary hybrid coding to solve the model, and verified the correctness of the proposed model and the effectiveness of the algorithm through an instance. This study provided an effective decision-making method for parallel machine job-shop scheduling problems. 11-29-2022 <![CDATA[ <p>The integrated scheduling of production and maintenance can make equipment maintenance in line with the production pace, so as to effectively prevent anormal interruptions of the production process due to equipment failure, and ensure the smooth implementation of the production scheduling plan. Aiming at the parallel machine job-shop environment, and considering stochastic machine failures and different degradation speeds of parallel machines, this paper introduced the minimal maintenance and preventive maintenance strategies to establish an integrated scheduling model for production and maintenance, designed a genetic algorithm based on process coding and binary hybrid coding to solve the model, and verified the correctness of the proposed model and the effectiveness of the algorithm through an instance. This study provided an effective decision-making method for parallel machine job-shop scheduling problems.</p> ]]> Integrated Scheduling of the Production and Maintenance of Parallel Machine Job-shop Considering Stochastic Machine Breakdowns zhiyuan zhao qilong yuan doi: 10.56578/jemse010103 Journal of Engineering Management and Systems Engineering 11-29-2022 Journal of Engineering Management and Systems Engineering 11-29-2022 2022 1 1 Article 15 10.56578/jemse010103 https://www.acadlore.com/article/JEMSE/2022_1_1/jemse010103 https://www.acadlore.com/article/JEMSE/2022_1_1/jemse010102 Human societies and researchers ensued that the continuation of a one-dimensional development focused on economic benefits can endanger the survival and tranquility of humanity, after experiencing a period of economic development and due to the advantages and disadvantages of this type of development. Concerns and damages of the environment and social challenges have led to the evolution of a three-dimensional concept of development based on economy, environment and society being known as sustainable development. Due to different indicators in each dimension of sustainability, finding effective ones is substantial. Supply chains are one of the most important and comprehensive domains in which sustainability led to better integration of layers and improve the total performance. On the other hand, current literatures demonstrate serious gap in representing comprehensive and integrated guidelines in order to optimize environmental and social indicators impacts in the management of supply chain. In this paper, all possible indicators for sustainability are collected, mapped into the layers of supply chain and inserted to a proposed mathematical model. The outputs are the effective indicators in three dimensions of sustainability for all layers of supply chain maximizing the sustainability of the whole supply chain. The proposed approach is implemented in a fishery supply chain. 11-29-2022 <![CDATA[ <p>Human societies and researchers ensued that the continuation of a one-dimensional development focused on economic benefits can endanger the survival and tranquility of humanity, after experiencing a period of economic development and due to the advantages and disadvantages of this type of development. Concerns and damages of the environment and social challenges have led to the evolution of a three-dimensional concept of development based on economy, environment and society being known as sustainable development. Due to different indicators in each dimension of sustainability, finding effective ones is substantial. Supply chains are one of the most important and comprehensive domains in which sustainability led to better integration of layers and improve the total performance. On the other hand, current literatures demonstrate serious gap in representing comprehensive and integrated guidelines in order to optimize environmental and social indicators impacts in the management of supply chain. In this paper, all possible indicators for sustainability are collected, mapped into the layers of supply chain and inserted to a proposed mathematical model. The outputs are the effective indicators in three dimensions of sustainability for all layers of supply chain maximizing the sustainability of the whole supply chain. The proposed approach is implemented in a fishery supply chain.</p> ]]> Mathematical Modeling for Sustainability Evaluation in a Multi-Layer Supply Chain hamed fazlollahtabar doi: 10.56578/jemse010102 Journal of Engineering Management and Systems Engineering 11-29-2022 Journal of Engineering Management and Systems Engineering 11-29-2022 2022 1 1 Article 2 10.56578/jemse010102 https://www.acadlore.com/article/JEMSE/2022_1_1/jemse010102 https://www.acadlore.com/article/JEMSE/2022_1_1/jemse010101 11-29-2022 <![CDATA[ ]]> Editorial to the Inaugural Issue dragan marinkovic dragan pamucar doi: 10.56578/jemse010101 Journal of Engineering Management and Systems Engineering 11-29-2022 Journal of Engineering Management and Systems Engineering 11-29-2022 2022 1 1 Article 1 10.56578/jemse010101 https://www.acadlore.com/article/JEMSE/2022_1_1/jemse010101