A Study of Multi-criterion Fuzzy Decision-making Methods: Recent Trends and Directions

Abstract

A plausible soft computing model for addressing ambiguity and vagueness in decision-making circumstances is the concept of intuitionistic fuzzy sets (IFSs). Utilizing similarity-distance metrics, cases like diagnostic analysis, Finance and Investment, pattern recognition, Risk Analysis and Assessment, etc. have been investigated. Numerous methods of distance and similarity have been suggested and utilized to solve deciding circumstances. Although the existing similarity measures and their distance counterparts are fairly significant, they have certain accuracy and conceptual alignment issues that need to be addressed in order to improve output reliability. As a result, a unique similarity-distance technique is introduced in this paper. To demonstrate the benefits of the innovative similarity-distance strategy over related current approaches, a comparative analysis is presented. The theoretical and philosophical aspects of the approach are set aside, and the analysis is solely focused on the algorithmic (technical) point of view. Applying the ranking algorithm yields a solution. A comparative study is presented to illustrate the advantages of the suggested measures. The outcomes of applying the suggested similarity metrics are confirmed by a technique known as Topsis. The outcomes are more logical, consistent, and productive in a skeptical setting.