Abstract
The simplified neutrosophic set (SNS) is a generalization of the fuzzy set that is designed for some incomplete, uncertain and inconsistent situations in which each element has different truth membership, indeterminacy membership and falsity membership functions. In this paper, we propose the simplified neutrosophic correlated averaging (SNCA) operator and the simplified neutrosophic correlated geometric (SNCG) operator, and further study the properties of the operators. Then, an approach to multi-attribute group decision making (MAGDM) within the framework of SNS is developed by the proposed aggregation operators. Finally, a practical application of the developed approach to the problem of investment is given, and the result shows that our approach is reasonable and effective in dealing with uncertain decision making problems.
Highlights
Fuzzy set was introduced by Zadeh, which has been widely used in decision making, artificial intelligence, pattern recognition, information fusion, etc. [1, 2, 6]
We have proposed simplified neutrosophic correlated averaging (SNCA) and simplified neutrosophic correlated geometric (SNCG) operators based on the related research on the intuitionistic fuzzy values (IFVs)
We have developed a method for addressing the multi-attribute group decision making (MAGDM) problem expressed with simplified neutrosophic set (SNS)
Summary
Fuzzy set was introduced by Zadeh, which has been widely used in decision making, artificial intelligence, pattern recognition, information fusion, etc. [1, 2, 6]. Peng defined the novel operations and proposed some aggregation operators for SNSs, developed a method to MAGDM [17]. Based on Xu’s work, Wei proposed some induced correlated aggregating operators for intuitionistic fuzzy values or interval-valued intuitionistic fuzzy values with correlative weights and applied them to MAGDM [19]. We utilize a numerical example to validate the proposed decision making methods, particular emphasis is put on addressing aggregation operators on SNSs. The result shows that our approach is reasonable and effective in dealing with uncertain decision making problems.
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