An integrated Pythagorean fuzzy fairly operator-based MARCOS method for solving the sustainable circular supplier selection problem

Due to intensified sensitivity towards environmental protection and social responsibility, the concept of sustainable development has been widely mentioned in various industries. Circular economy has received great attention as it meets environmental and social requirements. It contributes to better resource efficiency and a more sustainable economic development by means of its principles to gain strategic advantages. Choosing suitable supplier in view of the circular and sustainability aspects are of great importance for all firms. Thus, the purpose of this study is to integrate the Pythagorean fuzzy information-based fairly aggregation operators, the CRiteria Importance Through Intercriteria Correlation (CRITIC), the PIvot Pairwise RElative Criteria Importance Assessment (PIPRECIA) and the Measurement of Alternatives and Ranking based on COmpromise Solution (MARCOS) methods to assess and rank the sustainable suppliers in circular supply chains. The proposed study classified into four phases. First, some fairly aggregation operators for Pythagorean fuzzy numbers (PFNs) are introduced that include the concept of proportional distribution to achieve a fair treatment to the membership and non-membership degrees of PFNs. Second, a combination of PF-CRITIC and PF-PIPRECIA-based weight-determining formula is introduced to compute the relative significances of the considered sustainability indicators. Third, the PF-MARCOS based on proposed PF-fairly aggregation operator and combined weight-determining tool is proposed to rank the sustainable circular suppliers (SCSs). Further, an illustrative case study of SCS selection is discussed to prove the practicability and effectiveness of the PF-MARCOS method. For this purpose, this study considers an inclusive set of four key criteria and 25 sub-criteria in the related literature concerning SCS selection process. The assessment criteria are categorized into economic, circular, environmental and social dimensions based on the expert’s opinions. The result shows the significance degrees of economic, circular, environmental and social dimensions are 0.1964, 0.277, 0.3237 and 0.203, respectively and the SCS-4 (M4) should be chosen as the most suitable choice among others for the given data. The reliability and robustness of the presented methodology are examined by means of the comparative and sensitivity analyses. The findings of this study make a significant contribution to the SCS process by providing a novel decision support system from uncertainty perspective.