Complex intuitionistic fuzzy distance measures with hesitance value and their applications in decision making

Abstract

In applicationsrequiring uncertain, imprecise, and multi-dimensional data,where traditional distance measuresfrequently fallshort of capturing the full complexity of interactions among elements, a distance measure for complex intuitionistic fuzzy sets(DMCIFSs) becomes essential. Although DMCIFSs have been developed, most of them do not account forthe hesitation degree, which is crucial for capturing ambiguity and uncertainty in human reasoning.As extensions of the normalized Hamming and Euclidean distance measures, thiswork proposestwo new measures namely the Hesitance DMCIFSs(HDMCIFSs) and the Euclidean Hesitance DMCIFSs(EHDMCIFSs). These newly proposed measures provide a more comprehensive framework for modeling uncertainty by explicitly incorporating the hesitancy component.In addition to the proposed measures,several fundamental procedures and theoreticalresults are also presented. Furthermore, a novel decision-making method utilizing these distance measuresis developed and applied to multicriteria decision-making (MCDM) problems. The effectiveness of the proposed methodsis demonstrated through a comparative study, highlighting their potential forimproved sensitivity and accuracy in practical decision-making scenarios.