T-Spherical Fuzzy Information and Schweizer-Sklar Operations-Based Maclaurin Symmetric Mean Operator and Their Applications

Abstract

T-spherical fuzzy set (TSFS) is the generalization of the fuzzy set (FS) that extracts information from real-life scenarios with certainty. Aside from the remarkable advantage of being able to account for the connections among the multi-input considerations, such as multi-attributes or multi-experts in the multi-attribute group decision-making (MAGDM), the Maclaurin symmetric mean operator (MSMO) is also the generalization of several different existing operators. Moreover, one important class of T-norms (TN) and T-conorms (TC) is the Schweizer-Sklar TN (SSTN) and TC (SSTC). In this article, the operational laws for TSFS based on SSTN and SSTC are introduced first. Then the introduced operations are used to develop a class of aggregation operators (AOs) to aggregate the information in the form of the T-spherical fuzzy values (TSFVs). The introduced operators in this article are the T-spherical fuzzy Schweizer-Sklar MSMO (TSFSSMSMO) and the T-spherical fuzzy Schweizer-Sklar weighted MSMO (TSFSSWMSMO). Further, the TSFSSMSMO and TSFWMSMO are applied to a specific multi-attribute group decision-making (MAGDM) problem to show the significance of the developed operators.