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

This paper presents three novel single-valued neutrosophic soft set (SVNSS) methods. First, we initiate a new axiomatic definition of single-valued neutrosophic similarity 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. Moreover, we develop the combined weights, which can show both the subjective information and the objective information. Later, we propose three algorithms to solve single-valued neutrosophic soft decision making problem by Evaluation based on Distance from Average Solution (EDAS), similarity measure and level soft set. Finally, the effectiveness and feasibility of approaches are demonstrated by a numerical example.