Parametric Similarity Measures on Linguistic Single-Valued Neutrosophic Sets with Application to Decision-Making Problems

Linguistic single-valued neutrosophic (LSVN) set (LSVNS) is one of the influential contrivance for addressing the decision-making (DM) problems with uncertain and qualitative information by means of degree of acceptance, indeterminacy and non-acceptance in linguistic terms. In DM problems, similarity measure is the basic tool for recognizing associations within or across the given choices. Thus, this paper aims to construct the parametric similarity measure and weighted parametric similarity measure by making use of LSVNSs. The basic axioms of these measures are also highlighted. Further, the manuscript offers the multi-criteria DM approach based on the proposed measures and describes it by a numerical example. Finally, the efficiency and its preferences over the existing methods are confirmed by means of sensitivity and comparative investigation.

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