A novel integrated Multi-Criteria Decision-Making (MCDM) framework was proposed to address the complex challenge of assessing renewable energy performance. The framework incorporates the Modified Standard Deviation (MSD) method and the Criteria Importance Through Intercriteria Correlation (CRITIC) approach to objectively determine the weights of performance indicators, while the Ranking of Alternatives by the Weights of the Criteria (RAWEC) method was applied to derive annual performance rankings. A real-time case study covering Turkey over the period 2015–2023 was conducted to validate the proposed model. A total of ten criteria were identified to comprehensively evaluate the renewable energy performance of Turkey. The empirical findings revealed that the average annual growth rate of installed renewable power capacity, the share of electricity generated from renewables in total electricity generation, and the absolute quantity of electricity produced from renewable sources exerted the greatest influence on performance outcomes. According to the RAWEC-based ranking, the year 2023 emerged as the most successful in terms of renewable energy advancement during the observed period. These findings provide critical insights for policymakers and stakeholders, supporting evidence-based decision-making for enhancing energy security, achieving environmental sustainability, and guiding national energy strategy. The proposed integrated framework demonstrates a robust, data-driven approach that may be adapted to other national contexts or timeframes to support the monitoring, evaluation, and strategic planning of renewable energy systems. Ultimately, the study contributes to the broader discourse on sustainable development and climate change mitigation by offering a replicable and scalable assessment methodology.
Supply chain digitalization (SCD) has been recognized as a critical enabler of high-quality development in the manufacturing sector. To explore its influence mechanisms, an SCD indicator was constructed through textual analysis of corporate disclosures by Chinese manufacturing firms listed on the Shanghai and Shenzhen A-share markets from 2008 to 2022. Based on the theoretical lens of supply chain integration, the impact of SCD on high-quality development was empirically examined. The findings indicate that SCD significantly promotes high-quality development across manufacturing firms. Further analysis revealed that this relationship is positively mediated by two core mechanisms: supply chain collaborative innovation and the advancement of supply chain finance (SCF). These mediating effects were found to be strengthened under conditions of heightened environmental dynamism, underscoring the adaptive value of digital supply chain capabilities in volatile contexts. Heterogeneity analysis demonstrated that the positive effects of SCD are more pronounced in non-state-owned enterprises, firms in growth or decline stages, and those characterized by low levels of resource slack. Additionally, the long-term economic consequences of SCD were evaluated, and it was observed that enhanced digitalization contributes to the stable growth of firms’ long-term value by reinforcing their high-quality development trajectories. By clarifying the pathways through which SCD influences development outcomes, this study offers empirical evidence that enriches the existing body of literature on digital transformation within supply chains. Moreover, practical implications are provided for policy formulation and strategic decision-making aimed at fostering digitally integrated, innovation-driven, and financially resilient manufacturing ecosystems.
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.
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.
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