Supplier evaluation and selection in a sustainable supply chain based on fuzzy-BWM, entropy method and grey relational TOPSIS
2023 (English)In: Journal of Intelligent & Fuzzy Systems, ISSN 1064-1246, E-ISSN 1875-8967, Vol. 44, no 6, p. 9919-9932Article in journal (Refereed) Published
Abstract [en]
Suppliers significantly affect the effectiveness of sustainable supply chain management. Hence, it is extremely important to evaluate and select suppliers scientifically and objectively. Based on the theory of triple bottom line (economic, social, and environmental dimension) and a balanced scorecard, a measureable supplier evaluation framework in a sustainable supply chain is first formulated. Second, to reduce the defects of the single weight method, the subjective and objective weights of evaluation indicators are determined by combining the fuzzy best-worst method (BWM) and the entropy method, and then the combination weights are obtained through linear weighting. Third, the grey relational technique for order performance by similarity to ideal solution (TOPSIS) method is further adopted to evaluate and rank the suppliers. Finally, a case study illustrates and demonstrates the availability of the proposed supplier evaluation index system and evaluation method. Subsequently, some suggestions are proposed according to the results.
Place, publisher, year, edition, pages
IOS Press, 2023. Vol. 44, no 6, p. 9919-9932
Keywords [en]
Sustainable supply chain management, supplier evaluation, the fuzzy BWM, grey relational, TOPSIS
National Category
Production Engineering, Human Work Science and Ergonomics Other Mechanical Engineering
Research subject
Quality Technology and Logistics
Identifiers
URN: urn:nbn:se:ltu:diva-99291DOI: 10.3233/JIFS-212996ISI: 001004176300069Scopus ID: 2-s2.0-85166674216OAI: oai:DiVA.org:ltu-99291DiVA, id: diva2:1786844
Note
Validerad;2023;Nivå 2;2023-08-10 (joosat);
Funder: National Natural Science Foundation of China (Nos.71971064 and 71801045); Social Science Foundation of Fujian Province (No. FJ2022B071); National Natural Science Foundation of Fujian Province (No. 2020J01460)
2023-08-102023-08-102024-07-02Bibliographically approved