Exponential function-driven single-valued neutrosophic entropy and similarity measures and their applications to multi-attribute decision-making

Abstract

Single-valued neutrosophic set is an important tool for describing fuzzy information and solving fuzzy decision problems. It is known that entropy can be applied to measure the degree of uncertainty of evaluation information and determine the important degree of objects, similarity is mainly used to capture the internal relationship of the evaluation objects. Therefore, single-valued neutrosophic entropy and single-valued neutrosophic similarity are two important topics in multi-attribute decision-making (MADM) problems. In this paper, some new single-valued neutrosophic entropy and similarity methods are first proposed to deal with uncertain and fuzzy decision problems with the help of exponential function. Then, the proofs of exponential entropy and exponential similarity measures fit the definition of single-valued neutrosophic similarity and single-valued neutrosophic entropy are presented. Moreover, we apply these two measure methods to cope with the MADM problems, then a new MADM method is provided. Finally, the developed MADM method is applied to the practical example of investment decision, and comparisons with other methods are conducted to show the advantages and rationality of our method.