A NOVEL HYBRID APPROACH FOR SIMPLIFIED NEUTROSOPHIC DECISION-MAKING WITH COMPLETELY UNKNOWN WEIGHT INFORMATION

Abstract

The simplified neutrosophic set (SNS) is a useful model to describe the indeterminacy information which widely exists in the real world. In this paper, we develop a multicriteria decision-making (MCDM) method under simplified neutrosophic environment in which the information about weights of criteria is completely unknown, and the decision criterion values take the form of simplified neutrosophic numbers (SNNs). In order to determine the weighting vector of the criteria, we establish an optimization model based on the basic ideal of the traditional gray relational analysis (GRA) method. By solving this model, we get a simple and exact formula which can be used to determine the criterion weights. Moreover, we utilize the dice similarity measure to determine the similarity measures between each alternative decision and the related ideal decisions. Then, based on the traditional GRA method and the technique for order preference by similarity to ideal solution (TOPSIS), some calculation steps are presented for solving a simplified neutrosophic multicriteria decision-making problem with completely unknown weight information. To avoid information loss, this model does not use the aggregation process of decision information. Comparisons of the suggested methodology with other methods are also made. Finally, a numerical example and an experimental analysis are proposed to illustrate the application of the proposed model.