MCDM problem under single-valued neutrosophic numbers based on a novel similarity measure

One of the challenges in MCDM with a neutrosophic environment is the lack of an efficient analysis tool to rank the alternatives. Aggregative operators, ranking methods, distance measures, and similarity measures are among the most essential tools for sorting fuzzy data and their extensions, including Neutrosophic numbers. Although most of the existing methods can rank the candidates in the MCDM problem under certain initial conditions, they also have shortcomings from some points of view. On the other hand, very few papers have used the similarity measure to sort the alternatives in the MCDM problem under trapezoidal Neutrosophic numbers. Thus, the main objective of this study is to introduce a novel similarity measure for sorting alternatives. For this purpose, we first point out some shortcomings related to the existing ranking approach for numbers neutrosophic. After introducing the novel similarity measure, several critical properties of the suggested similarity measure between neutrosophic numbers are investigating. The proposed similarity measure offers us a proper way to apply the TOPSIS method in the context of neutrosophic information. The efficiency and utility of the proposed similarity measure are illustrated by a comparative example.