Maclaurin Symmetric Mean Aggregation Operators Based on Spherical Fuzzy Information and Application to Decision-Making

Abstract

In contemporary information fusion theory, the Maclaurin symmetric mean (MSM) operator is a traditional mean type aggregation operator (AO) that is an appropriate-able technique for aggregating numerical quantities. The MSM operator’s ability to record the relationships between the several input arguments is one of its standout features. The spherical fuzzy set (SFS) is also a remarkable technique that covers the maximum information from real-life scenarios with the help of four grades. This manuscript consists of the development of the MSM and weighted MSM for the information obtained by SFS. Consequently, the spherical fuzzy MSM (SFMSM) and spherical fuzzy weighted MSM (SFWMSM) operators are developed, and their basic properties are studied. Finally, the developed SFMSM and SFWMSM operators have been applied to the real-life problem of the multi-attribute decision-making problem. All the results are compared and then clearly tabulated and graphed.

Received: 24 November 2022 | Revised: 6 January 2023 | Accepted: 23 January 2023

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.