Ensuring the integrity of goods during cold chain transportation remains a critical challenge in logistics, as it is essential to preserve product quality, freshness, and compliance with stringent safety standards. Strategic decision-making in this context requires the prioritization of customer requirements and the optimal allocation of limited operational resources. In response to these demands, an integrated Multi-Criteria Decision-Making (MCDM) model was developed by combining the Best-Worst Method (BWM), Quality Function Deployment (QFD), and Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) approach. Within this framework, BWM was utilized to determine the relative importance of user requirements, which were then mapped onto specific operational resources through QFD to identify critical resource elements and derive their corresponding weights. These weights, subsequently treated as evaluation criteria in the MARCOS method, were applied to assess the performance of Third-Party Logistics (3PL) providers. The proposed methodology was validated through a case study involving eight user requirements and seven key resources. The findings indicated that precise temperature control and delivery speed were the most critical user requirements, whereas advanced temperature sensors and vehicles with cooling systems were identified as the most significant resources. Based on the MARCOS evaluation, Provider 1 emerged as the most optimal 3PL alternative. This integrated decision-making model offers a systematic and data-driven approach for aligning customer priorities with resource capabilities, thereby enabling logistics providers to enhance service quality, operational efficiency, and strategic competitiveness in temperature-sensitive supply chains. The model also demonstrates practical scalability and adaptability across diverse cold chain scenarios.
The enhancement of governance and the implementation of effective anti-corruption strategies are critical for fostering public trust, accountability, and transparency in developing countries. In this study, a structured approach was adopted to identify and prioritize key strategies for improving governance and combating corruption in Nigeria. An extensive literature review, supplemented by expert consultation, led to the identification of eight fundamental strategies. To systematically determine their relative significance, the Fermatean Fuzzy Stepwise Weight Assessment Ratio Analysis (FF-SWARA) method was employed. The findings indicate that strengthening the legal and regulatory framework through effective enforcement, judicial reforms, and the establishment of independent oversight bodies with legal protection and operational autonomy are the most impactful measures. These strategies are essential for enhancing public trust, accountability, and transparency in Nigeria. The insights derived from this study provide a robust foundation for policymakers and stakeholders seeking to implement targeted anti-corruption reforms in Nigeria and other developing economies facing similar governance challenges.
Linear systems often involve coefficients that are uncertain or imprecise due to inherent variability and vagueness in the data. In scenarios where only approximate or vague knowledge of the system parameters is available, traditional fuzzy logic is commonly employed. However, conventional fuzzy logic may be inadequate when defining a membership degree with a single, precise value proves difficult. In such cases, Single-Valued Trapezoidal Neutrosophic Numbers (SVTrNNs) offer a more suitable framework, as they account for indeterminacy, alongside truth and falsity. The solution of Single-Valued Trapezoidal Neutrosophic Linear Equations (SVTrNLEs) was explored in this study using an embedding approach. The approach reformulates the SVTrNLEs into an equivalent crisp linear system, enabling the application of conventional solution methods. The solution was then obtained using either the matrix inversion method or the gradient descent optimization algorithm implemented in PyTorch. The robustness and adaptability of gradient-based optimization techniques were thoroughly assessed. The learning process minimizes the residual error iteratively, with convergence behaviour and numerical stability analyzed across various parameter configurations. The results demonstrate rapid convergence, proximity to exact solutions, and significant robustness to parameter variability, highlighting the efficacy of gradient descent for solving uncertain linear systems. These findings provide a foundation for the extension of gradient-based methods to more complex systems and broader applications. Furthermore, the existence and uniqueness of the neutrosophic solution to an $n\times n$ linear system were rigorously analyzed, with numerical examples provided to assess the reliability and efficiency of the proposed methods.
This study investigates the application of Multi-Criteria Decision-Making (MCDM) techniques in fruit production, specifically focusing on the use of the interval fuzzy rough pivot pairwise relative criteria importance assessment (PIPRECIA) method for criteria evaluation. A total of 11 criteria were evaluated to rank various combinations of plum varieties and rootstocks. The criteria selected represent key aspects of plum production, including phenology, yield, physical fruit characteristics, and the chemical composition and quality of the fruit. Data for the study were collected through surveys of 17 experts and plum producers. The results indicated that the criteria related to overall yield and fruit weight were deemed the most significant, while those concerning the chemical composition and fruit quality were considered of lesser importance. The findings highlight the potential of the interval fuzzy rough PIPRECIA method in addressing both research and managerial challenges in fruit production. It is suggested that future research expand the application of this method to other geographical regions and agricultural sectors. Additionally, the development of accessible software tools featuring user-friendly interfaces could facilitate broader adoption of MCDM techniques in agricultural decision-making.
The transportation of dangerous goods (DG) presents significant risks due to their hazardous chemical properties, which, in the event of an accident, can have detrimental effects on the environment, public health, and infrastructure. Although the transport of such materials is generally prohibited, the growing demand for DG transportation over long distances necessitates compliance with stringent international regulations (e.g., ADR, RID). In urban areas, where transport routes may intersect with residential zones, incidents involving DG can lead to severe consequences, including fatalities, environmental damage, evacuation of local populations, and disruptions to traffic. To mitigate these risks, effective risk management is essential, encompassing analysis, assessment, and reduction strategies. Risk assessment for DG transport can be conducted using various quantitative and qualitative methods, with this study employing the Areal Locations of Hazardous Atmospheres (ALOHA) software and Geographic Information System (GIS) tools for both risk evaluation and visualization. The study area is located in the capital of Montenegro, specifically within the Stari Aerodrom District. This research focuses on evaluating the potential impact of DG transport incidents in this area and the consequences of hazardous material releases in confined spaces. Three specific DGs—benzene, chlorine, and methane—are considered, each presenting distinct environmental, health, and property-related risks. Chlorine is selected as the worst-case scenario, with its impact radius extending approximately 10 km from the release point. The primary objective of this study is to provide a comprehensive assessment of the risks associated with DG transportation, highlighting the importance of safety improvements and effective emergency response strategies. The findings underscore the need for enhanced safety measures during transport and the development of more robust emergency management frameworks for DG-related incidents.
In today’s volatile and competitive global markets, organizations face numerous challenges to their survival and growth. To navigate these challenges effectively, the adoption of future-oriented, environment-based planning strategies is essential. Such strategies must not only address the identification of key environmental factors but also assess their long-term impacts on the organization, alongside its interaction with these external variables. The survival and sustainable development of an organization depend on a timely understanding of emerging opportunities and market dynamics, the formulation of strategic plans, and the selection of appropriate, effective strategies. This study presents an integrated model designed to evaluate the factors influencing a construction company’s performance, with a focus on conducting a comprehensive risk analysis. The model prioritizes and quantifies the significance of each element within the strengths, weaknesses, opportunities, and threats (SWOT) analysis of the company’s operational context. Furthermore, two fuzzy logic-based Multiple-Attribute Decision-Making (MADM) methods, namely the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and the Analytic Hierarchy Process (AHP), were employed to rank the identified factors. Based on the analysis of the collected data, the final strategic course for the company was derived. The results indicated that the TOPSIS method placed a greater emphasis on the organization's strengths and opportunities, while the AHP approach, despite prioritizing long-term safety considerations, underscored the significance of addressing weaknesses and mitigating threats. This research contributes to the understanding of how fuzzy MADM techniques can be applied to strategic planning in the construction industry, facilitating more informed decision-making processes that align with the evolving demands of the market and ensure organizational resilience.
The inherent hierarchical and decentralized nature of decision-making within banking systems presents significant challenges in evaluating operational efficiency. This study introduces a novel bi-level programming (BLP) framework, incorporating Stackelberg equilibrium dynamics, to assess the performance of bank branches. By combining with data envelopment analysis (DEA), the proposed BLP-DEA model captures the leader-follower relationship that characterizes banking operations, wherein the leader focuses on marketability and the follower prioritizes profitability. A case study involving 15 Iranian bank branches was employed to demonstrate the model’s capacity to evaluate performance comprehensively at both decision-making levels. The results underscore the model's effectiveness in identifying inefficiencies, analyzing cost structures, and providing actionable insights for performance optimization. This approach offers a robust tool for addressing the complexities associated with decentralized decision-making in hierarchical organizations. The findings have significant implications for both theoretical development and practical application, especially in the context of improving the operational efficiency of banking institutions.
The identification of optimal stock or portfolio options is a critical concern for investors aiming to maximize profitability within financial markets. With the increasing complexity of available alternatives and the growing volume of financial data, selecting the most suitable investment has become more challenging. Decision-makers often face difficulties in navigating these vast data sets and require robust support tools to simplify and enhance the decision-making process. This study proposes a three-phase approach designed to reduce data complexity and facilitate more detailed analysis. In the initial phase, firms demonstrating low operational efficiency, as indicated by their inventory turnover ratio, were excluded from further consideration. In the subsequent phase, data envelopment analysis (DEA) was employed to assess the efficiency of remaining firms, with those exhibiting efficiency scores lower than one being removed from further investigation. Finally, the third phase involved determining the relative importance of each financial ratio through the calculation of their respective weights, allowing for the ranking of firms based on these adjusted values. The results of this approach provide decision-makers with a refined list of viable investment options, contributing to more informed stock portfolio optimization decisions.
Fire, as an unpredictable and highly destructive hazard, poses significant risks to densely populated environments such as employee dormitory buildings. This study aims to evaluate fire risks in such facilities and propose effective fire safety management strategies to enhance fire prevention capabilities and evacuation efficiency. An index system of fire risk influencing factors specific to employee dormitory buildings was established through an extensive review of relevant literature and field interviews. The Ordinal Priority Approach (OPA), a multi-attribute decision analysis method based on ordinal data, was employed to quantify the weights of these influencing factors using a linear programming model. Subsequently, fire scenarios were simulated using PyroSim software, focusing on the top two critical influencing factors to assess evacuation times and safety conditions. The analysis identified the condition of fire-fighting facilities, ventilation within dormitory buildings, the use of high-power electrical appliances, and smoking behaviors among employees as key determinants of fire risk. The simulation results indicated that visibility during a fire significantly affects the available safe evacuation time. While natural ventilation was found to moderately mitigate fire spread, its impact was less pronounced compared to the effectiveness of automatic sprinkler systems. The reliability of the simulation outcomes was further validated through expert interviews, ensuring the practical applicability of the findings. Based on the outcomes of risk analysis and scenario simulations, several fire safety improvement measures were proposed. These include upgrading fire-fighting facility standards, optimizing natural ventilation systems, and implementing comprehensive fire safety education and training programs. The insights derived from this research provide a robust scientific foundation and actionable recommendations for the fire risk management of employee dormitory buildings.
The Logistics Performance Index (LPI) represents a tool developed by the World Bank that is used to measure the efficiency and effectiveness of a country’s logistics sector, and comprises of six components. This indicator is used to compare the logistics performance of different countries, identify challenges in global supply chains, and help policymakers improve their logistics infrastructure and service quality. Given the importance of this indicator, every country aims to achieve a higher LPI score and, consequently, a more favorable ranking. The objective of this paper is to propose a new methodology for calculating the LPI score for transport routes. To validate the proposed methodology, the study analyzes seven cases involving import and export flows from Serbia. Based on the results, the analysis identifies which transport routes achieve the highest scores and which require specific preventive and corrective actions to improve their performance.
When the word "disaster" is used, it usually refers to both human-caused situations that have a negative impact on the community and its environment as well as natural disasters like hurricanes, earthquakes, floods, and similar phenomena. Good logistics management is crucial to reducing the bad effects of these kinds of circumstances. This typically entails tasks like planning, organizing, acquiring, moving, and other associated duties. The distribution of supplies to impacted individuals in an effort to save lives is the main objective of humanitarian logistics. The location of humanitarian goods and equipment, which are kept in makeshift humanitarian logistics centers, is crucial for ensuring prompt response in such circumstances. As a result, when deciding where to locate these centers, it is crucial to take into account particular local factors. Numerous factors might impact this kind of selection, which is why finding a location for a humanitarian logistics center is considered a multi-criteria challenge. This research suggests using the ADAM (Axial-Distance-Based Aggregated Measurement Method) and SWARA (Stepwise Weight Assessment Ratio Analysis) techniques to solve this kind of issue. An example of their application is provided by a case study that centers on where Serbia's humanitarian logistics hub is located. The creation of a framework and a special set of standards for choosing the locations of humanitarian logistics centers are the main results of this study. This can help decision-makers, authorities, individuals, non-governmental groups, and logistical service providers make well-informed decisions that have the potential to save countless lives.
This paper investigates the search for an exact analytic solution to a temporal first-order differential equation that represents the number of customers in a non-stationary or time-varying $M / D / 1$ queueing system. Currently, the only known solution to this problem is through simulation. However, a study proposes a constant ratio, $\beta$ (Ismail's ratio), that relates the time-dependent mean arrival and mean service rates, offering an exact analytical solution. The stability dynamics of the time-varying $M / D / 1$ queueing system are then examined numerically in relation to time, $\beta$, and the queueing parameters. On another note, many potential queueing-theoretic applications to traffic management optimization are provided. The paper concludes with a summary, combined with open problems and future research pathways.
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