A New Method of Distance Measure Between Intuitionistic Fuzzy Sets and its Application in Admission Procedure

Abstract

Sundry complex real-world problems involving decision-making have been resolved using the idea of distance measures between intuitionistic fuzzy sets (IFSs). Several distance measuring techniques between IFSs have been developed, but it is only the work of Xie et al. that considered the tendency coefficients of the intuitionistic fuzzy parameters (IFPs), namely, membership grade, nonmembership grade and hesitation grade. Albeit, Xie et al.’s technique uses assumed tendency coefficients for IFPs, which is defective for a reliable result. Sequel to this setback, we develop a new distance measure between IFSs which includes tendency coefficients of the IFPs, where the tendency coefficients are computed from the intuitionistic fuzzy values to enhance reliable results. In addition, the new distance measure between IFSs is applied to discuss students’ admission process to ascertain the most eligible candidate based on academic performance in an entry examination. The application is carried out using two techniques, namely, the recognition principle and the multiple criteria decision-making approach, respectively. Finally, the superiority of the newly developed distance measure between IFSs is shown comparatively with respect to the existing approaches between IFSs. This new distance measure can be applied to clustering analysis, multiple attributes decision-making (MADM), a technique for order preference by similarity to ideal solution (TOPSIS), etc. in future research.