Multiple attribute group decision-making method with completely unknown weights based on similarity measures under single valued neutrosophic environment

This paper proposes distance-based similarity measures of single valued neutrosophic sets (SVNSs) and their multiple attribute group decision-making method with completely unknown weights for decision makers and attributes under single valued neutrosophic environment. In the group decision-making method, two weight models based on the similarity measures are introduced to derive the weights of the decision makers and the attributes from the decision matrices represented by the form of single valued neutrosophic numbers (SVNNs) to decrease the effect of some unreasonable evaluations because the decision makers may have personal biases and some individuals may give unduly high or unduly low preference values with respect to their preferred or repugnant objects. Then, we introduce the weighted similarity measure between the evaluation value (SVNS) for each alternative and the ideal solution (ideal SVNS) for the ideal alternative to rank the alternatives and select the best one(s). Finally, an illustrative example is given to demonstrate the application and effectiveness of the developed method.