Algorithms for neutrosophic soft decision making based on EDAS and new similarity measure

This paper presents two novel single-valued neutrosophic soft set (SVNSS) methods. First, we initiate a new axiomatic definition of single-valued neutrosophic simlarity measure, which is expressed by single-valued neutrosophic number (SVNN) that will reduce the information loss and remain more original information. Then, the objective weights of various parameters are determined via grey system theory. Combining objective weights with subjective weights, we present the combined weights, which can reflect both the subjective considerations of the decision maker and the objective information. Later, we present two algorithms to solve decision making problem based on Evaluation based on Distance from Average Solution (EDAS) and similarity measure. Finally, the effectiveness and feasibility of approaches are demonstrated by a numerical example.