Decision analysis for data analytical enterprises with an innovative approach of spherical fuzzy prioritized aggregation operators

Abstract

In the rapidly evolving landscape of data analytics, effective decision-making is paramount. Multi-attribute decision-making (MADM) techniques are widely applied to handle uncertainty and vagueness in diverse fields such as medical diagnosis, social selection, networking, and environmental sciences. This article explores the concept of spherical fuzzy sets (SFSs) as a means to manage uncertain expert information more accurately. In addition, we modify prioritized aggregation operators by incorporating the operational laws of Einstein t-norm and t-conorm. The primary focus of this study is to develop a new family of mathematical models, namely spherical fuzzy Einstein prioritized average, spherical fuzzy Einstein prioritized weighted average, spherical fuzzy Einstein prioritized geometric and spherical fuzzy Einstein prioritized weighted geometric operators. Several reliable properties are also demonstrated to validate and establish the proposed operators. Furthermore, a hybrid decision-making model for the MADM problem is constructed to address complex real-life applications. An experimental case study is presented to evaluate suitable data analytical models based on specific criteria and mathematical approaches. To show the superiority and efficiency of the proposed methodologies, a comparative analysis is conducted against existing approaches.