DISTANCE AND SIMILARITY MEASURES FOR DUAL HESITANT FUZZY SOFT SETS AND THEIR APPLICATIONS IN MULTICRITERIA DECISION MAKING PROBLEM

The dual hesitant fuzzy set is one of the successful extensions of the fuzzy set in which elements are represented in terms of a set of possible values instead of a single number. However, their theory is restricted to their parameterized tool and hence it cannot be effectively applied to a real-life problem. In order to handle it, dual hesitant fuzzy soft set theory has been utilized in this manuscript and hence, based on it, some axioms of distance and similarity measures based on Hamming, Euclidean, and Hausdorff metrics have been proposed here. Various desirable relations between them have also been presented. The proposed distance measures are applied to the field of decision-making under dual hesitant fuzzy soft set environment. Finally, practical examples of pattern recognition and medical diagnoses are given to demonstrate the effectiveness of the proposed measures. A comparative study as well as advantages of the proposed distance measures over existing measures have been presented.