Multiple-attribute decision-making method using similarity measures of single-valued neutrosophic hesitant fuzzy sets based on least common multiple cardinality

In some decision situations, decision makers sometimes cause this difficult problem with a few different single-valued neutrosophic values assigned by truth, indeterminacy, and falsity degrees due to decision makers' hesitancy. Then, a single-valued neutrosophic hesitant fuzzy set (SVN-HFS) can express the hesitant information. Under a single-valued neutrosophic hesitant fuzzy environment, this paper introduces the extension method based on least common multiple cardinality for single-valued neutrosophic hesitant fuzzy elements (SVN-HFEs) and proposes the distance and similarity measures of SVN-HFSs. Then, we develop a multiple attribute decision-making (MADM) method by using the proposed similarity measure of SVN-HFSs. Finally, an illustrative example of investment alternatives is given to demonstrate the application and feasibility of the developed approach. The main advantage of the developed method is that it is more objective and more universal than the existing ones.