Generalized hesitant fuzzy knowledge measure with its application to multi-criteria decision-making

Abstract

Hesitant fuzzy (HF) entropy and HF-knowledge measures are two dual concepts that have similar practical applications despite different mathematical structures. In some real-life scenarios, one particular entropy/knowledge measure may not be reasonable due to some undesirable and counter-intuitive situations. In this paper, we introduce a one-parametric generalized knowledge measure in the HF-setting. Such a generalization provides a class of HF-knowledge measures and hence the flexibility in the practical problems. We show the advantages of the generalized measure over the existing HF-entropy/knowledge measures in view of weight computation in the decision-making problems and ambiguity computation of two different hesitant fuzzy elements. At last, we apply the proposed generalized knowledge measure of HFSs in multi-criteria decision-making (MCDM) using the bidirectional projection method in the hesitant fuzzy environment.