Multi-Attribute Decision-Analytic Approach based on Spherical Fuzzy Rough Schweizer-Sklar Aggregation Operators with Applications in Agricultural Management Systems
Mehwish Sarfraz, Rizwan Gul · Management Science Advances. · 2025
To find reasonable solutions for complex issues, multi-attribute group decision-making is an essential method that considers relevant attributes. For this purpose, the Schweizer–Sklar t-norms and t-conorms offer flexible and effective aggregation operators. Meanwhile, prioritized aggregation operators integrate critical information from available data to further enhance decision-making. To address uncertainty and imprecision in decision-making, in this script, we explore the spherical fuzzy rough set theory. Motivated by the utility of the Schweizer–Sklar t-norms and t-conorms, we propose a range of novel aggregation operators specifically designed for spherical fuzzy rough values, including the spherical fuzzy rough Schweizer–Sklar weighted averaging and spherical fuzzy rough Schweizer–Sklar weighted geometric operators. We examine the fundamental properties of the proposed operators in detail and demonstrate how multi-attribute group decision-making can benefit from them. A numerical example in agricultural management systems is provided to illustrate how to select the best alternative based on the given criteria. Finally, we compare the outcomes obtained using these newly postulated operators with those derived from existing studies in the literature to validate the effectiveness and practicality of the designed approaches.