Similarity-Distance Decision-Making Technique and its Applications via Intuitionistic Fuzzy Pairs

Abstract

The idea of intuitionistic fuzzy sets (IFSs) is a reasonable soft computing construct for resolving ambiguity and vagueness encountered in decision-making situations. Cases such as pattern recognition, diagnostic analysis, etc., have been explored based on intuitionistic fuzzy pairs via similarity-distance measures. Many similarity and distance techniques have been proposed and used to solve decision-making situations. Though the existing similarity measures and their distance counterparts are somewhat significant, they possess some weakness in terms of accuracy and their alignments with the concept of IFSs, which needed to be strengthened to enhance reliable outputs. As a consequent, this paper introduces a novel similarity-distance technique with better performance rating. A comparative analysis is presented to showcase the advantages of the novel similarity-distance over similar existing approaches. Some attributes of the similarity-distance technique are presented. Furthermore, the applications of the novel similarity-distance technique in sundry decision-making situations are explored.