A Novel Modified ARAS Approach for Evaluating Sustainable Green Supply Chains: A Case Study in the Food Industry
محورهای موضوعی : مجله بین المللی ریاضیات صنعتی
1 - Department of Mathematics, Islamic Azad University, Semnan Branch, Semnan, Iran.
کلید واژه: Multi-Criteria Decision-Making, Modified ARAS Method, Sustainable Supply Chain, Green Supply Chain Evaluation, Food Industry,
چکیده مقاله :
[This paper introduces a novel framework for evaluating the performance of sustainable green supply chains using the Modified Additive Ratio Assessment (ARAS-M) method, an enhanced version of the traditional ARAS approach. A comprehensive literature review identified key factors impacting green supply chain sustainability, including environmental impact, energy efficiency, waste management, and resource optimization. The proposed ARAS-M method incorporates a linear programming model to optimize the weighting process, making it adaptable for future development under fuzzy logic, which allows for improved modeling of uncertainty in decision-making. The method was applied to assess six food industry companies' supply chains, with expert input to rank these companies based on identified attributes. Validation was achieved by comparing the rankings to real-world performance metrics such as environmental certifications and resource consumption reports. Results indicated that companies excelling in environmental policies, efficient energy use, and waste reduction ranked highest. This study demonstrates the effectiveness of the ARAS-M method in evaluating green supply chain performance and highlights the importance of sustainability practices in enhancing supply chain resilience and competitiveness.
[This paper introduces a novel framework for evaluating the performance of sustainable green supply chains using the Modified Additive Ratio Assessment (ARAS-M) method, an enhanced version of the traditional ARAS approach. A comprehensive literature review identified key factors impacting green supply chain sustainability, including environmental impact, energy efficiency, waste management, and resource optimization. The proposed ARAS-M method incorporates a linear programming model to optimize the weighting process, making it adaptable for future development under fuzzy logic, which allows for improved modeling of uncertainty in decision-making. The method was applied to assess six food industry companies' supply chains, with expert input to rank these companies based on identified attributes. Validation was achieved by comparing the rankings to real-world performance metrics such as environmental certifications and resource consumption reports. Results indicated that companies excelling in environmental policies, efficient energy use, and waste reduction ranked highest. This study demonstrates the effectiveness of the ARAS-M method in evaluating green supply chain performance and highlights the importance of sustainability practices in enhancing supply chain resilience and competitiveness.
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