Multi-criteria decision making based on novel distance measure in intuitionistic fuzzy environment

Abstract

In comparison to fuzzy sets, intuitionistic fuzzy sets are much more efficient at representing and processing uncertainty. Distance measures quantify how much the information conveyed by intuitionistic fuzzy sets differs from one another. Researchers have suggested many distance measures to assess the difference between intuitionistic fuzzy sets, but several of them produce contradictory results in practice and violate the fundamental axioms of distance measure. In this article, we introduce a novel distance measure for IFSs, visualize it, and discuss its boundedness and nonlinear characteristics using appropriate numerical examples. In addition to establishing its validity, its effectiveness was investigated using real-life examples from multiple fields, such as medical diagnosis and pattern recognition. We also present a technique to solve pattern recognition problems, and the superiority of the proposed approach over existing approaches is demonstrated by incorporating a performance index in terms of "Degree of Confidence" (DOC). Finally, we extend the applicability of the proposed approach to establish a new decision-making approach known as the IFIR (Intuitionistic Fuzzy Inferior Ratio) method, and its efficiency is analyzed with other established decision-making approaches.