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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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