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.
This study examines the relationship between the blue economy and food security in lower-middle-income ASEAN countries, specifically Indonesia, Cambodia, the Philippines, and Vietnam, over the period 2012–2022. While the blue economy holds significant potential for enhancing food security, its implementation is often hindered by environmental degradation, limited access to renewable energy, inadequate technological advancements, insufficient investment, and rapid population growth. By employing Ordinary Least Squares regression and a system of simultaneous equations, key interactions among environmental quality, renewable energy utilization, technological innovation, investment, and demographic dynamics are analyzed. The findings reveal that improvements in environmental quality foster the adoption of renewable energy, while technological advancements significantly contribute to the expansion of the blue economy. Furthermore, the development of the blue economy is identified as a critical driver of food security, with investment and effective population management playing essential roles in ensuring its long-term sustainability. The results indicate that a comprehensive strategy integrating environmental protection, technological progress, and renewable energy adoption is essential for enhancing food security through the blue economy. Based on these insights, policy recommendations are proposed, emphasizing the need for stringent emission controls, increased investment in renewable energy, promotion of technological innovation, and sustainable demographic policies. These measures are expected to facilitate a resilient blue economy, ensuring food security and long-term socio-economic stability in ASEAN’s lower-middle-income nations.
This study investigates the levels of biodiversity awareness and conservation behaviours among science teacher candidates and examines the extent to which these levels vary across demographic and academic variables. A survey-based research design was employed, involving 216 teachers candidates enrolled in the Science Education program within the Department of Mathematics and Science Education at the Faculty of Education. Data were collected using the Biodiversity Awareness Measurement Tool (BAMT) and the Biodiversity Behaviour Measurement Tool (BBMT). Analysis revealed that the mean biodiversity awareness score was 3.57 ± 0.328, whereas the mean conservation behaviour score was 3.53 ± 0.370. A statistically significant gender-based difference was observed in biodiversity awareness, with female participants exhibiting higher awareness levels; however, no significant difference was detected in conservation behaviours. Class level was found to exert a partial influence on both awareness and behaviour scores. Notably, 93.5% of participants reported never having engaged in biodiversity-related activities, indicating a substantial gap between awareness and active conservation efforts. This disconnect underscores a critical challenge in translating theoretical knowledge into practical engagement in biodiversity preservation. Biodiversity is fundamental to ecosystem stability, species sustainability, and human well-being, yet it remains under threat due to rapid urbanisation, industrial pollution, agricultural chemical use, and deforestation. Given the role of educators in fostering environmental consciousness, it is imperative that teacher candidates receive comprehensive training in biodiversity conservation and sustainable ecosystem management. While theoretical knowledge is essential, active participation in conservation initiatives is equally crucial. Greater emphasis should be placed on experiential learning approaches that immerse students in ecosystems, foster direct engagement with nature, and cultivate a sense of responsibility for biodiversity protection. It is recommended that environmental education curricula incorporate nature-based activities, ecological restoration projects, and biodiversity monitoring programs. Furthermore, teacher candidates should be encouraged to participate in sustainability initiatives, field-based environmental studies, and community-led conservation efforts. By fostering a deeper connection with nature and embedding biodiversity conservation into educational practice, future generations of educators can be equipped to promote environmental stewardship and instil sustainable values in their students, thereby contributing to the long-term preservation of global biodiversity.
With the increasing global emphasis on sustainability, understanding the factors influencing tourists’ green travel intentions (GTI) has become a crucial area of research. This study examines the determinants of GTI, with a particular focus on environmental attitudes (ATE), subjective norms (SN), environmental concerns (EC), environmental knowledge (EK), and green marketing strategies (GM). The green marketing framework is further delineated into green services (GPR), green advertising (GPM), green distribution (GPL), and green pricing (GPC). A quantitative research design was employed, utilizing a structured survey administered to 600 tourists in Vietnam through convenience sampling. The findings reveal that both ATE and GM exert a significant influence on GTI. Moreover, ATE mediates the effects of GM, EC, and EK on GTI, highlighting its central role in shaping pro-environmental travel behavior. Additionally, SN is identified as a moderating factor in the relationship between ATE and GTI, indicating that societal influences reinforce the impact of individual ATE on green travel choices. These findings provide theoretical contributions by advancing the understanding of psychological and marketing-driven influences on sustainable tourism behavior. From a practical perspective, the results underscore the importance of well-structured green marketing initiatives in fostering environmentally responsible travel behavior. Tourism industry stakeholders are encouraged to integrate comprehensive GM that enhance environmental awareness and promote sustainable tourism practices. Future research directions are also proposed, including the examination of longitudinal behavioral changes and cross-cultural validations.
The mitigation of road traffic accidents remains a critical global challenge, particularly in regions where cultural norms and behavioral risk factors significantly influence driving practices. This study employs a hybrid Multi-Criteria Decision-Making (MCDM) approach, integrating Grey Theory, the Full Consistency Method (FUCOM), and the Evaluation based on Distance from Average Solution (EDAS), to systematically assess four strategic interventions: Infrastructure Improvements, Educational Programs, Policy Amendments, and Technology Integration. These strategies are evaluated based on a set of criteria that encompass attitudes toward speeding, perceptions of traffic laws, the use of safety equipment, and the prevalence of high-risk driving behaviors. The findings indicate that while Infrastructure Improvements and Technology Integration enhance the physical and technological dimensions of road safety, Educational Programs and Policy Amendments play an indispensable role in shaping driver behavior and reinforcing compliance with traffic regulations. The necessity of a comprehensive and integrated strategy that leverages both technological advancements and behavioral interventions is underscored, ensuring a holistic and sustainable reduction in traffic-related fatalities and injuries. The outcomes of this study provide valuable insights for policymakers and road safety authorities, offering a structured framework for the prioritization and implementation of road safety measures tailored to the socio-cultural and behavioral dynamics of Libya.
The optimization of railway train selection in Pakistan has become increasingly critical due to rapid population growth and rising travel demands. Despite efforts by the Railway Transport (RT) Department to enhance efficiency, productivity, and safety through policy reforms and infrastructure advancements, persistent challenges such as outdated technology, infrastructure bottlenecks, frequent delays, and inadequate maintenance continue to hinder progress. Addressing these issues is imperative to ensuring sustainable, efficient, and resilient railway operations. Given the multifaceted and uncertain nature of railway system modeling and management, decision-making (DM) processes necessitate robust methodologies capable of handling imprecise and ambiguous data. In this study, an innovative DM framework is introduced, leveraging intuitionistic fuzzy sets (IFSs) as an advanced extension of fuzzy sets (FSs) to manage uncertainty and hesitation in complex scenarios. By employing Einstein t-norm and t-conorm-based operators, novel operational laws for intuitionistic fuzzy credibility numbers (IFCNs) are proposed. Three key aggregation techniques—Confidence Intuitionistic Fuzzy Credibility Einstein Weighted Averaging (CIFCEWA), Confidence Intuitionistic Fuzzy Credibility Einstein Ordered Weighted Averaging (CIFCEOWA), and Confidence Intuitionistic Fuzzy Credibility Einstein Hybrid Weighted Averaging (CIFCEHWA) operators—are developed to provide a structured approach for processing and analyzing intuitionistic fuzzy data. To evaluate the practical applicability and reliability of the proposed methodology, a structured DM algorithm is formulated and validated using a real-world railway train selection case study. The incorporation of confidence levels within the IFCN framework enhances DM precision by quantifying the degree of certainty, thereby reducing risk and improving reliability. The findings demonstrate that the proposed approach effectively addresses the inherent uncertainties in railway selection processes, leading to more informed and strategic planning. Furthermore, the applicability of IFCNs extends beyond railway systems, offering valuable insights for domains such as artificial intelligence, financial DM, management science, and engineering, where uncertainty plays a pivotal role.
The instability of mining waste dumps poses significant environmental hazards, including loss of life, damage to infrastructure, and ecological degradation. The complex interdependence of Thermal, Hydraulic, and Mechanical (THM) processes has been increasingly recognised as a critical factor influencing slope stability. In this study, a coupled THM numerical model was developed using the finite element method (FEM) to evaluate slope stability in a coal mine waste dump in Maamba, Zambia. Key parameters, including stress distribution, displacement, pore water pressure, and temperature variations, were incorporated to achieve a comprehensive assessment of slope failure mechanisms. Field data and geotechnical investigations were integrated with advanced computational simulations to ensure realistic modelling. The findings demonstrated that conventional limit equilibrium methods (LEM) underestimated the impact of coupled processes on slope failure. The safety factor was observed to decrease by more than 30% due to THM interactions, with thermal gradients and hydro-mechanical (H-M) responses identified as primary contributors to slope instability. The results underscore the necessity of incorporating THM coupling in slope stability assessments, particularly in geotechnically sensitive mining environments. The proposed framework provides a scientifically grounded methodology for evaluating and mitigating landslide risks in mining waste dumps, offering valuable insights applicable to regions with similar geotechnical and climatic conditions. The findings contribute to the refinement of slope stability management strategies and provide a basis for the development of risk mitigation measures in vulnerable mining areas.
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.
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.
The European Union has actively promoted the development of intermodal transport as a response to the ecological and safety challenges posed by global transportation flows. Through projects such as Customer-driven Rail-freight services on a European mega-corridor based on Advanced business and operating Models (CREAM) and Reorganization of Transport Networks through Advanced Rail Freight Concepts (RETRAC), substantial improvements in rail freight transport on European mega-corridors have been achieved. These initiatives aim to enhance transportation efficiency, reduce environmental impacts, and improve safety. The proximity of Serbia to these key corridors, coupled with its involvement in these projects, presents an opportunity for the country to enhance its infrastructure, expand its business prospects, and increase its global market competitiveness. Furthermore, such developments will contribute significantly to Serbia’s economic growth and its integration into European transportation networks. This study evaluates the CREAM and RETRAC corridors by examining their effects on environmental sustainability, transportation safety, and economic development. The paper also assesses their overall efficiency and sustainability, providing valuable insights for strategic decision-making. Given the complex nature of intermodal freight corridors, the evaluation process incorporates Multi-criteria decision making (MCDM) techniques, considering a variety of performance indicators. Specifically, the hybrid model combining the Fuzzy Aggregated Distance-Based Measurement (FADAM) method and the Fuzzy Best-Worst Method (FBWM) is applied to offer a comprehensive analysis. This approach allows for the systematic assessment of the two corridors, supporting the development of strategies aimed at optimizing their performance and sustainability.
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.
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