Evaluation and selection strategy for green supply chain using interval-valued q-rung orthopair fuzzy combinative distance-based assessment

• Main Authors: Khan, S.A.R., Mathew, M., Dominic, P.D.D., Umar, M.
• Format: Article
• Published: Springer Science and Business Media B.V. 2022
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117405979&doi=10.1007%2fs10668-021-01876-1&partnerID=40&md5=4beb9ce0d9071f678d1c21fbd3d5d7bf; http://eprints.utp.edu.my/33532/

Environmental deterioration and global warming has created a substantial impact on international companies to incorporate eco-friendly, green supply chain practices and remain competitive in the market. In food supply chain, the selection of the best supply chain management (SCM) strategy can improve the overall performance and can be crucial in managing the food supply chain challenges and achieving sustainable food supply chain. In this paper, different green SCM strategies were explored and evaluated for food supply chain using combinative distance-based assessment method under interval-valued q-rung orthopair fuzzy information as interval extension of q-rung orthopair fuzzy can effectively handle the ambiguity and uncertainty of preference given by experts in decision making. A generalized p-distance between two interval-valued q-rung orthopair fuzzy numbers is proposed in the current study and is used to formulate interval-valued q-rung orthopair fuzzy Hamming distance and interval-valued q-rung orthopair fuzzy Euclid distance. Five supply chain strategies, namely risk-based, efficiency-based, resource-based, innovation-based, and closed-loop strategy, are evaluated to select best strategy for food supply chain based on different attributes such as green manufacturing, green design and development, green management system, green procurement, and green marketing which contribute toward sustainability. It is found that closed-loop supply chain strategy is the best strategy for food supply chain to attain sustainability. A comparative analysis and sensitivity analysis are done to examine the reliability of results obtained from the proposed framework. © 2021, The Author(s), under exclusive licence to Springer Nature B.V.