multiple-criteria decision-making
Fatemeh Mojibian; Maryam Daneshvar; Ehsan Kafash Abdi
Abstract
With the rapid expansion of digital technologies and the ongoing transformation of financial services, FinTech has emerged as a key driver of change in the banking industry. Banks, as core components of the financial system, face fundamental shifts in service delivery, customer behavior, and competitive ...
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With the rapid expansion of digital technologies and the ongoing transformation of financial services, FinTech has emerged as a key driver of change in the banking industry. Banks, as core components of the financial system, face fundamental shifts in service delivery, customer behavior, and competitive patterns following the integration of FinTech. However, the adoption of these technologies is inherently associated with multiple risks that may affect banks’ performance, security, and reputation. Thus, timely identification and effective management of FinTech-related risks are essential. This study aims to evaluate the critical risks of FinTech adoption in banks using a multi-criteria decision-making (MCDM) framework in a neutrosophic environment, with a case study on Pasargad Bank. First, relevant risk factors were identified through an extensive literature review. Using the neutrosophic Delphi method, seven major risks were confirmed: security, credit, operational, strategic and competitive, legal and regulatory, reputational, and liquidity risks. The relative importance of these risks was then assessed using the neutrosophic Best–Worst Method (BWM). The results highlight security risk as the most significant factor influencing FinTech adoption in Pasargad Bank. Finally, various FinTech implementation scenarios were ranked using the neutrosophic Multi-Attributive Border Approximation Area Comparison (MABAC) method, with the scenario of “collaboration with other banks to establish a FinTech consortium” receiving the highest priority. The findings provide valuable insights for banks to better understand critical risk dimensions and to select optimal strategies for the successful implementation of FinTech solutions.
multiple-criteria decision-making
Iraj Rouhi; Mahsa Pishdar; Maryam Hassanikordede
Abstract
Food loss and waste represent a major global challenge threatening food security and exacerbating climate change. Upcycling food waste into value-added products is increasingly recognized as an effective pathway toward a circular economy. This study introduces a novel integrated multi-criteria decision-making ...
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Food loss and waste represent a major global challenge threatening food security and exacerbating climate change. Upcycling food waste into value-added products is increasingly recognized as an effective pathway toward a circular economy. This study introduces a novel integrated multi-criteria decision-making (MCDM) framework based on Circular Intuitionistic Fuzzy Sets (CIFS) combined with MEREC objective weighting and CIFS-MARCOS ranking an approach not previously applied to food waste upcycling. Ten prominent upcycling strategies were identified from recent literature and evaluated against twelve sustainability criteria by ten food industry experts. Results revealed “market potential,” “public awareness of upcycled products,” and “food quality and safety” as the most influential criteria. Among strategies, producing sustainable textiles from food waste ranked first, followed by sustainable packaging, novel food ingredients, and bioenergy production. The proposed framework effectively handles uncertainty and dynamic interdependencies among criteria, offering a robust and original tool for prioritizing upcycling pathways. Findings provide policymakers and industry stakeholders with evidence-based guidance to maximize environmental, economic, and social benefits while supporting multiple Sustainable Development Goals (SDGs).
multiple-criteria decision-making
Mojtaba Farrokh
Abstract
Nowadays, despite the growing awareness of evaluating suppliers based on sustainability aspects, there are still limitations in selecting suppliers according to sustainable performance due to the lack of a comprehensive list of sustainability criteria and well-developed methods for assessing them. Given ...
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Nowadays, despite the growing awareness of evaluating suppliers based on sustainability aspects, there are still limitations in selecting suppliers according to sustainable performance due to the lack of a comprehensive list of sustainability criteria and well-developed methods for assessing them. Given the complexity and uncertainty surrounding these criteria and the supplier evaluation process, along with the need for high precision and sensitivity, this study aims to apply a rough-fuzzy DEMATEL-TOPSIS approach to rank meat and livestock suppliers. This hybrid method is designed to manage both internal and external uncertainties as well as the complexity of sustainability criteria. In the first phase, the rough-fuzzy DEMATEL method is used to determine the weights and interrelationships among the criteria. In the second phase, the rough-fuzzy TOPSIS method is employed to rank the suppliers. The applicability of this approach is examined through a case study in the meat supply chain. The results reveal that five criteria—cost, livestock health and meat freshness, impact on the local community, delivery reliability, and technological capability—are the most influential factors in selecting sustainable suppliers.
Introduction
The selection of sustainable suppliers for meat and livestock has become a central topic within the food supply chain (Mohammed, 2020; Islam et al., 2024). Given the increasing global concerns regarding climate change, improving animal welfare conditions, and ensuring food quality and safety, the need for suppliers adhering to sustainability principles is becoming more urgent. Sustainable suppliers must not only comply with environmental requirements but also pay special attention to social and economic aspects to meet customer and community needs (Masudin et al., 2024; Singh et al., 2025). This paper examines the criteria and challenges associated with selecting sustainable suppliers in the meat and livestock industry and discusses innovative methods for evaluating and improving their performance.
This study proposes a methodology for selecting sustainable suppliers by developing a rough-fuzzy DEMATEL-TOPSIS approach that considers internal and external uncertainties. This approach offers several advantages: first, it combines fuzzy and rough sets—merging internal and external uncertainty management (Chen et al., 2019). Second, the proposed rough-fuzzy method simplifies the understanding of uncertainty using convex polygons. Third, the integration of fuzzy sets and rough sets provides a clear approach to managing various types of uncertainties, reducing distortions that could lead to incorrect outcomes (Chen et al., 2019; Stević et al., 2025). In this research, the Shahrvand Company was selected as a case study.
Literature review
Regarding the selection of meat and livestock suppliers, although selection criteria have been well developed, there is no consensus on the number of criteria or the overarching theory that defines the sustainability criteria chosen. Alikhani and colleagues (2019) show that strategic meat supplier selection is a multifaceted process that requires considering various factors, including sustainability and risk, yet no comprehensive research has simultaneously addressed both factors. According to them, traditional decision models in this area cannot distinguish sufficiently between different candidates, especially under conditions involving subjective judgments and separate criteria for each supplier. This study presents a multi-criteria approach utilizing fuzzy sets and a Data Envelopment Analysis (DEA) model that considers risk and sustainability simultaneously in supplier evaluation. Mohammed (2020) indicates that evaluating and selecting meat suppliers based on sustainability, involving environmental, social, and economic criteria, requires multi-criteria decision-making methods and integrated fuzzy multi-criteria techniques. This research develops an integrated approach based on fuzzy multi-criteria techniques for assessing, selecting, and optimally allocating suppliers in the meat supply chain, contributing to more comprehensive and sustainable decision-making processes. Khan and Ali (2021) demonstrate that selecting a sustainable supplier in the meat distribution chain involves analyzing multiple factors, including environmental, economic, and social dimensions. Furthermore, innovative methods such as interpretive structural modeling (ISM) and fuzzy VIKOR were employed to identify key factors and evaluate suppliers in the Pakistani meat chain.
Developing a systematic methodology that simultaneously considers these two types of internal and external uncertainties is essential. To address this issue, Chen et al. (2019) used rough-fuzzy sets within a DEMATEL-ANP framework to evaluate the needs for sustainable value in product service systems, providing a valuable reference for managing internal and external uncertainties concurrently.
Methodology
In this study, after gathering the effective criteria for supplier evaluation from a review of the literature in reputable databases and through interaction with the planning, commercial, production, research and development, and quality control departments of the studied company, a total of 14 sustainability indicators were selected. Additionally, five supplier companies were evaluated as decision-making model options. One of the objectives of this research is to examine the interrelationships among sustainability criteria across economic, social, and environmental dimensions. The proposed approach introduces a new framework for evaluating and selecting suppliers based on sustainability criteria. In the first stage, the rough-fuzzy DEMATEL method is used to determine the internal relationships among these criteria and their weights. In the second stage, the rough-fuzzy TOPSIS method is employed to rank the suppliers. The use of fuzzy numbers allows for consideration of external and internal impacts in selecting a sustainable supplier, providing more precise information for decision-making regarding the criteria and improving the accuracy of the ranking results (Chen et al., 2019).
Discussion and conclusion
Based on the rankings derived from the rough-fuzzy DEMATEL method, the five top criteria are cost, livestock health and meat freshness, impact on the local community, delivery reliability, and technological capability, in that order. The analysis and ranking of the factors influencing the selection of sustainable suppliers for meat supply show that cost, livestock health, and meat freshness are the highest priority criteria. These findings suggest that organizational managers should primarily focus on controlling and improving factors related to cost and product quality, namely livestock health and meat freshness, as they directly affect customer satisfaction, supply process effectiveness, and organizational credibility. The criteria related to impact on the local community and delivery reliability also hold significant importance but are ranked lower; this indicates that, alongside quality and cost, special attention should be given to social interactions, especially considering the requirements for development and long-term sustainability.
Therefore, organizations should consider strategies that not only focus on economic criteria but also emphasize social aspects, enabling them to identify and develop leading and sustainable suppliers in competitive markets. This ranking also provides suppliers with insights to recognize their weaknesses and areas needing improvement, guiding them toward performance enhancement and alignment with sustainability goals.
multiple-criteria decision-making
mahdi mashhadikhani; alireza poorebrahimi; mostafa moballeghi
Abstract
Artificial intelligence, through process optimization, productivity enhancement, and cost reduction, has created a significant transformation in production, management, and innovation. Accordingly, the present study aims to examine the mutual effects of parameters influencing the adoption of AI-based ...
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Artificial intelligence, through process optimization, productivity enhancement, and cost reduction, has created a significant transformation in production, management, and innovation. Accordingly, the present study aims to examine the mutual effects of parameters influencing the adoption of AI-based technologies: A case study of Kerman Motor Company. The research method is applied in terms of purpose and survey-based in terms of data collection. Data were gathered through the distribution of 130 questionnaires among the employees of Kerman Motor Automotive Company, selected by simple random sampling using Cochran’s formula. The measurement instruments were the technology adoption questionnaires developed by Chatterjee et al. (2021) and Shon & Vawn (2020). For data analysis, structural equation modeling (SEM) was employed. The findings indicated that employees’ subjective norms have a positive and significant effect on perceived usefulness and perceived ease of use of AI-based technologies in the automotive company. Perceived usefulness positively and significantly affects employees’ behavioral intention and attitudes toward the use of AI-based technologies. Perceived ease of use positively and significantly influences employees’ attitudes toward the use of AI-based technologies, and attitudes positively and significantly influence behavioral intention. Finally, behavioral intention to use AI-based technologies in the automotive company has a positive and significant effect on actual use. Overall, the results revealed that the effect of all research variables was positive and significant, and all research hypotheses were supported.
multiple-criteria decision-making
Ali Memarpour Ghiaci; morteza abbasi; Jafar Gheidar-Kheljani
Abstract
Abstract
Supplier evaluation plays a pivotal role in the success of modular megaprojects, as these projects require capable suppliers due to the necessity for complex coordination among various subsystems and the precise integration of modules. This study proposes an integrated framework for the evaluation ...
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Abstract
Supplier evaluation plays a pivotal role in the success of modular megaprojects, as these projects require capable suppliers due to the necessity for complex coordination among various subsystems and the precise integration of modules. This study proposes an integrated framework for the evaluation of suppliers in modular megaprojects. For the first time, this research applies a novel integrated approach based on the LOPCOW and ARTASI methods, extended using spherical fuzzy sets (SF-LOPCOW and SF-ARTASI) for supplier evaluation. Based on this approach, 31 sustainability-oriented criteria have been identified for evaluating suppliers in modular megaprojects. The criteria are first weighted using the SF-LOPCOW method. Subsequently, in a case study, 12 suppliers identified for a modular megaproject are evaluated and prioritized using the SF-ARTASI method. A comparison of the SF-ARTASI results with other existing multi-criteria decision-making methods in the literature, along with a sensitivity analysis, demonstrates the effectiveness of the proposed approach and the robustness of its results under different scenarios.
Introduction
With the rapid expansion of the global economy, investment in large-scale projects worldwide has increased markedly over the past few decades. Projects with costs of one billion dollars or more are recognized as megaprojects. Megaprojects are inherently associated with growth, development, and competitiveness, acting as the infrastructure of globalization. Modularization is a key driver for reducing the time and cost of megaprojects. With the modularization of megaprojects, the evaluation and selection of suppliers acquire particular importance. The question therefore arises: how can suppliers for modular megaprojects be evaluated in the long term while concurrently reducing project delays? The present study concentrates on this critical issue, which can assist project and megaproject managers from a sustainable development perspective. First, it is essential to collect core criteria from various dimensions—economic, environmental, and social—to evaluate a sustainable supplier; then, by employing a multi-criteria decision-making (MCDM) method, the relative importance of these criteria is determined, and suppliers are subsequently evaluated and prioritized. The supplier evaluation problem is complex and involves uncertainty across all sustainability dimensions (economic, environmental, and social).
The main objective of this study is to evaluate and prioritize suppliers of modular megaprojects by proposing a novel approach under uncertainty. This study intends, for the first time, to apply the developed SF-LOPCOW-ARTASI method to the supplier evaluation problem. This method is capable of handling both uncertainty and group decision-making simultaneously. In this research, the supplier evaluation problem for megaprojects is, for the first time, conducted based on sustainability dimensions within a spherical fuzzy environment. The approach is presented for the first time by using the LOPCOW method developed on the basis of spherical fuzzy sets (SF-LOPCOW) to weight the criteria, and the ARTASI method developed on the basis of spherical fuzzy sets (SF-ARTASI) to prioritize suppliers of modular megaprojects.
Method
The present study employs an integrated approach. In the first stage, supplier evaluation criteria are identified and, after defining the alternatives, data derived from the judgments of the decision-making team are collected as linguistic variables based on spherical fuzzy sets. Subsequently, following the evaluation of suppliers against the identified criteria, the criteria weights are calculated using the SF-LOPCOW method. Finally, by implementing the SF-ARTASI method, suppliers are assessed according to the criteria and prioritized. Using purposive sampling, the decision-making team consisted of eleven experts with experience and specialization in management systems implementation consultancy, engineering, and project and megaproject management. Information on the members indicates that the majority of the expert team have between eight and fourteen years of professional experience.
Discussion and Results
To illustrate the applicability of the proposed approach, suppliers for modular megaprojects were evaluated and prioritized using this approach. In this study, twelve suppliers were assessed and ranked using 31 evaluation criteria. First, each supplier was evaluated by the decision-making team according to the identified criteria using linguistic variables based on spherical fuzzy sets. Given the uncertainty inherent in the evaluation criteria, spherical fuzzy sets were employed to address this uncertainty. The relative importance of the criteria was then determined using the developed LOPCOW method based on spherical fuzzy sets. According to this method, cost, strategy and organization, and the amount of waste generated received the highest importance weights of 0.087, 0.083, and 0.079, respectively. Subsequently, using the proposed approach, suppliers were evaluated and prioritized by applying the developed ARTASI method based on spherical fuzzy sets, taking into account the evaluation criteria and their importance degrees. The results indicate that S3, S9, and S7 ranked first through third, respectively.
Finally, a sensitivity analysis was designed in the form of multiple scenarios to examine the relationship between the outcomes produced by the proposed approach under varying conditions and the study’s findings. This analysis investigated the variation in the final utility function and the resulting ranking of alternatives as the values of φ and α changed; in both cases, the ranges of variation were negligible and not statistically significant.
Conclusion
Due to the need for complex coordination among subsystems and precise integration of modules, the success of modular megaprojects largely depends on the evaluation and selection of capable suppliers. The present study introduces an integrated approach for supplier evaluation in modular megaprojects. Accordingly, a comprehensive list of sustainability criteria for evaluating and prioritizing suppliers of modular megaprojects was identified. The relative importance of these criteria was then determined using the SF-LOPCOW method. Subsequently, following the proposed approach, suppliers were evaluated and prioritized according to the criteria and their importance weights by applying the SF-ARTASI method. The limited number of experts in the field of megaproject management and the absence of weighting expert judgments according to their knowledge and experience represent limitations of this study. The use of aggregation operators to integrate expert judgments, such as the spherical weighted arithmetic mean (SWAM) operator, and the development and comparison of multi-criteria decision-making methods in other uncertain environments (e.g., Pythagorean fuzzy, q-rung, and Fermatean fuzzy sets), and comparing their results with the methods developed in the present study, are suggested for future research. Regardless of the case used to implement the proposed approach, the method is applicable to various supplier evaluation and selection scenarios for megaprojects. In future work, we will extend our research to optimize scheduling and reduce the completion time of modular megaprojects through the employment of appropriate suppliers.
multiple-criteria decision-making
Sara Asili; Ebrahim Teimoury
Abstract
Considering the competitive environment between suppliers, the issue of choosing them based on important criteria is very important for decision makers, especially in the LARS supply chain, which is a combination of sustainable and LARG supply chains. The aim of this paper is to present a multi-objective ...
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Considering the competitive environment between suppliers, the issue of choosing them based on important criteria is very important for decision makers, especially in the LARS supply chain, which is a combination of sustainable and LARG supply chains. The aim of this paper is to present a multi-objective mathematical model for selecting suppliers based on criteria related to the LARS supply chain concepts. The innovations of the presented model include simultaneous consideration of multiple objectives, multiple periods, multiple products, and master production schedule. The quality of the presented model has been examined on a case study in the country's automotive industry. In this paper, at first, the most important criteria have been extracted from the literature, then finalized by experts in the country's automotive industry, and DEMATEL approach has been used to examine the internal relationships of the criteria in each category of criteria. Secondly, the network of criteria is determined, the importance of each criterion relative to the others was determined using a pairwise comparison matrix and considered as input to the Super Decision software. At last, a mathematical model for optimal supplier selection is presented. Based on the results obtained, the ordering cost had the greatest impact on the objective function. Also, considering the concept of backlog demand has led to flexibility in production volume and, as a result, reduced overall costs.IntroductionLARG Supply Chain Management represents an integrated approach that combines lean, agile, resilient, and green paradigms in supplier relationship management. This holistic framework enables organizations to simultaneously leverage the advantages of each approach while compensating for their inherent limitations. For example, lean supply chain management focuses on minimizing inventory levels to reduce waste and cost; agile supply chain management emphasizes responsiveness and flexibility to meet dynamic market demands; resilient supply chain management enhances the ability to withstand and recover from disruptions; and green supply chain management aims to minimize environmental impacts and promote sustainability. According to Babaei et al. (2017), in an increasingly volatile and uncertain global environment, organizations are placing greater emphasis on supply chain resilience as a key capability for survival and competitiveness. Resilient supply chains are characterized by their capacity to absorb shocks and maintain operational continuity. Meanwhile, the concept of sustainable supply chain management—which addresses environmental, social, and economic concerns—has attracted growing interest among both academics and practitioners. More recently, researchers have begun to explore the LARS framework as a comprehensive model that overlaps with sustainability initiatives, particularly through its emphasis on green practices. However, despite their similarities, LARS and sustainability are distinct in scope and application. This study aims to identify a comprehensive set of criteria for supplier selection under the LARS framework, thereby supporting more informed and strategic decision-making in supply chain management. To this end, relevant criteria will be extracted through expert input from a leading firm in the Iranian automotive industry. In parallel, an extensive literature review will be conducted to incorporate practical and validated indicators associated with lean, agile, resilient, and sustainable supply chain practices. A Likert-scale-based questionnaire will be developed to assess suppliers against these criteria, and the resulting scores will serve as input for a multi-objective mathematical model.The remainder of this paper is structured as follows: Section 2 presents a review of the relevant literature. Section 3 introduces the proposed mathematical model. Section 4 details the case study and analyzes the results. Finally, Section 5 concludes the paper with key findings and managerial implications.MethodsIn this study, the most critical criteria were initially extracted through a thorough review of the existing literature and subsequently refined based on expert insights from the Iranian automotive industry, with a case study focused on Zamiad Company. To analyze the interrelationships among the criteria within each category, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method was employed. Following the identification of the criteria interaction network, the relative importance of each criterion with respect to the others was determined using pairwise comparison matrices. These comparisons served as input data for the Super Decisions software, facilitating the application of the Analytic Network Process (ANP). In the next phase of the study, a mathematical model was developed to support the optimal selection of suppliers.Discussion and resultsIn this research, the required data for all ten products were collected from the internal information systems of Zamiad Company. All collected data were evaluated in terms of consistency ratio, which was confirmed as being below 0.1, ensuring the reliability of the pairwise comparisons. After entering the data into Super Decisions software, the output—including the weights of each supplier for each product—were obtained. The results were reviewed and validated by experts at Zamiad Company.Although the proposed model is inherently nonlinear, it was linearized to reduce computational complexity. This adjustment significantly decreased the solution time, which is crucial when solving large-scale problems.The developed mathematical model aims to optimize the selection of suppliers for each product in each production period and to determine the allocated quantity to each supplier. The model incorporates various cost factors, including production, ordering, inventory holding, product shortage, and supplier switching costs. While the parameters of the model were intended to reflect actual company data, due to the confidentiality policies of Zamiad Company, some parameters were not accessible to the researchers. Therefore, these values were generated using normal distributions within predefined intervals based on expert judgment.The model was implemented over two six-month production periods, in alignment with Zamiad’s typical contractual arrangements with its suppliers. Based on the obtained results, ordering cost had the greatest impact on the objective function. Moreover, considering the concept of backlog demand has introduced flexibility in production volume, thereby reducing the overall costs.ConclusionThis study aimed to identify key criteria affecting supplier selection and to determine their relative importance within the integrated LARS supply chain approach. Based on the obtained results, incorporating the production planning process reduces the diversity of suppliers across different periods. This can be attributed to production integration and the model’s preference for maintaining existing contracts over frequent changes. Given the current economic conditions in the production environment, it is essential to consider all relevant parameters in the supplier selection process simultaneously. The findings indicate that accounting for production planning within the LARS framework leads to more effective supplier selection. Among the evaluated parameters, production cost and product ordering cost had a greater impact on the overall performance of the proposed model compared to other factors. Therefore, managerial strategies should focus on controlling these key cost components. This research has contributed by identifying and evaluating these critical parameters.
multiple-criteria decision-making
Mojtaba Hajian Heidary; Maede Mirzaaliyan
Abstract
In today's markets that industries have faced different risks and disruptions, selecting the appropriate and resilient supplier has become a strategic factor for the success and sustainability of organizations in a turbulent and competitive business environment and has attracted much attention from researchers ...
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In today's markets that industries have faced different risks and disruptions, selecting the appropriate and resilient supplier has become a strategic factor for the success and sustainability of organizations in a turbulent and competitive business environment and has attracted much attention from researchers and practitioners. The natural stone industry is one of the most important industries in Iran. Hence, this study aims to identify and rank the evaluation criteria of resiliency in a real case study of natural stone industry. Gathering the criteria was done based on the previous related literature and in order to confirm the identified criteria, a survey of 10 stone industry experts was conducted using the fuzzy Delphi method. Consequently, 20 criteria was approved. In order to rank the approved criteria, the best-worst method (BWM) was used. The results showed that flexibility, velocity and financial performance are the most important suppliers' resiliency evaluation criteria in the stone industry, respectively.
