2026-10-29T20:44:45Zhttps://munzur-rdlopen.rawdatalibrary.net/oai/request

oai:munzur-rdlopen.rawdatalibrary.net:munzur/22642026-10-28T15:29:34Zopenairecom_munzur_1col_munzur_2com_munzur_8col_munzur_154

A novel two-phase group decision-making model for circular supplier selection under picture fuzzy environmentKaya, Sema KayapinarAHPGroup decision-makingInterval grey numbersMARCOSPicture fuzzy setsCircular supply chain management (CSCM), which incorporates circular thinking into supply chain management, promotes supply chain sustainability by offering a novel and compelling viewpoint. In the CSCM, supplier selection is crucial in establishing a competitive edge among businesses by decreasing environmental degradation and related supply chain expenses. This paper aims to propose a novel two-phase group decision-making approach, which combines the picture fuzzy Analytical Hierarchical Process (PF-AHP) and grey Measurement of Alternatives and Ranking According to Compromise Solution (MARCOS-G) model to select railway material suppliers within the scope of circular economy perspective. In group decision-making, a picture fuzzy number-based approach aggregates individual decision makers’(DMs) opinions and provides a lower level of computing complexity and a higher level of performance. The PF-AHP has been used to produce the weights of the criteria, and the MARCOS-G technique has been used to rank the suppliers and choose the most appropriate one. The results and verification of the novel method are carried out throughout a comprehensive sensitivity analysis. For this purpose, 50 scenarios with changes in the weight values of criteria were developed. In the validation analysis, a comparison with other grey COPRAS, TOPSIS, ARAS and WASPAS methods were performed. Due to the ranking results obtained from Spearman’s correlation analyses, the MARCOS-G is almost consistent and it can be seen that the A4 remains the top supplier in all 50 scenarios.Springer Science and Business Media Deutschland GmbH2023Articlehttps://rdlopen.rawdatalibrary.net/r/munzur/2264https://doi.org/10.1007/s11356-022-24486-40944-1344enEnvironmental Science and Pollution Researchinfo:eu-repo/semantics/closedAccessinfo:eu-repo/semantics/articlehttps://munzur-rdlopen.rawdatalibrary.net/tr/items/munzur/2264