A New Single-valued Neutrosophic Distance for MABAC, TOPSIS and New Similarity Measure in Multi-attribute Decision-Making

Abstract

The aim of this paper is to define a new distance measure and apply it in three decision-making methods. First of all, we use single-valued neutrosophic numbers to describe the decision-making information, and proposes a new single- valued neutrosophic distance based on Hamming distance and Hausdorff distance. According to this new distance, a new similarity measure is initiated. Then we introduce three methods, which are TOPSIS, MABAC and similarity measure, to solve multi-attribute decision-making problem. Among these methods, the combined weight is obtained by both objective weight and subjective weight. After that, a numerical example is applied to figure out a ideal solution. Finally, we compare this result with other papers and discuss the effectiveness and reasonability.