A new exponential-logarithm-based single-valued neutrosophic set and their applications

The paper aims to present one of the extensions of the single-valued neutrosophic set, by utilizing the features of the exponential and logarithmic (EL) parameters. The present idea is to integrate the features of the EL operations into the standard neutrosophic set to enhance their diversity. The proposed set is named an exponential-logarithm single-valued neutrosophic set. The major feature of the proposed set is to include one attitude character parameter which can help the decision-maker to give their decision as per their needs. In the defined set, the uncertainties which are present during the analysis are handled with the help of the advancement of the neutrosophic set. Later on, we stated some basic operations to aggregate the information by defining the several weighted operators. The characteristics of these proposed operators are also illustrated in detail. In addition to it, we state the new exponential entropy measure and investigate the impact of the membership degrees of the proposed set. Finally, we construct a multi-attribute group decision-making algorithm to address the decision-making problems based on the stated concept. The attribute weights are derived by using exponential entropy measures. Several case studies are illustrated and the performance of the algorithm is compared with several existing studies. The sensitivity analysis and advantages of the proposed algorithm are also stated.

Automated summary

This paper presents an extension of the single-valued neutrosophic set by incorporating the features of exponential and logarithmic parameters. The proposed set, named exponential-logarithm single-valued neutrosophic set, includes a character parameter that allows decision-makers to make decisions according to their needs. Various weighted operators are defined to aggregate information, and the characteristics of these operators are illustrated. The paper also introduces a new exponential entropy measure and investigates the impact of membership degrees. Furthermore, a multi-attribute group decision-making algorithm is constructed, and its performance is compared with existing studies through case studies.