An Integrated Fermatean Fuzzy Group Decision Method based on Sugeno-Weber Operators for the Selection of Reverse Logistics Supplier
Implementing reverse logistics can not only save costs and reduce operational risks for enterprises, but also enhance their core competitiveness. However, how to choose the best reverse logistics supplier is one of the important decisions faced by enterprises, and it is also an urgent problem that modern enterprise management and logistics management need to solve. Hence, this paper proposed a novel multiple criteria group decision-making (MCGDM) approach based on coefficient of variation method, weighted aggregated sum product assessment (WASPAS) method and the proposed aggregation operators under Fermatean fuzzy setting. First, we define the Sugeno-Weber operations on Fermatean fuzzy number (FFN) and then propose four novel aggregation operators including, Fermatean fuzzy Sugeno-Weber weighted averaging operator, Fermatean fuzzy Sugeno-Weber weighted geometric operator and their related ordered weighted operators. Then the proposed Fermatean fuzzy Sugeno-Weber operators are used to integrate the Fermatean fuzzy assessment information provided by decision experts. Next, the coefficient of variation method is propounded based on the Fermatean fuzzy score function to estimate the importance of assessment criteria. Lastly, the improved WASPAS method is put forward to attain the sorting of alternatives. A cased study about the selection of green supplier is provided to discuss the effectiveness and feasibility of the proposed group decision method.
Authors
- Yuan Rong (ORCID: https://orcid.org/0000-0001-7576-5276)
- Guorou Wan
Institutions
- Ningxia Medical University (CN)
- Sichuan Institute of Building Research (CN)
- Sichuan College of Architectural Technology
Publication Details
- Journal
- Computer and decision making.
- Published
- 2025-03-15
- DOI
- https://doi.org/10.59543/comdem.v2i.10537
- Citations
- 4
- Primary Topic
- E-commerce and Technology Innovations
- Type
- article
- Field-Weighted Citation Impact
- 9.79