Hybrid Binary Logarithm Similarity Measure for MAGDM Problems under SVNS Assessments

Abstract

Single valued neutrosophic set is an important mathematical tool for tackling uncertainty in scientific and engineering problems because it can handle situation involving indeterminacy. In this research, we introduce new similarity measures for single valued neutrosophic sets based on binary logarithm function. We define two type of binary logarithm similarity measures and weighted binary logarithm similarity measures for single valued neutrosophic sets. Then we define hybrid binary logarithm similarity measure and weighted hybrid binary logarithm similarity measure for single valued neutrosophic sets. We prove the basic properties of the proposed measures.Then, we define a new entropy function for determining unknown attribute weights. We develop a novel multi attribute group decision making strategy for single valued neutrosophic sets based on the weighted hybrid binary logarithm similarity measure. We present an illustrative example to demonstrate the effectiveness of the proposed strategy. We conduct a sensitivity analysis of the developed strategy. We also present a comparison analysis between the obtained results from proposed strategy and different existing strategies in the literature.