q-Rung orthopair fuzzy sets, originally proposed by Yager, can dynamically adjust the range of indication of decision information by changing a parameter q based on the different hesitation degree, and point operator is a useful aggregation technology that can control the uncertainty of valuating data from some experts and thus get intensive information in the process of decision making. However, the existing point operators are not available for decision-making problems under q-Rung orthopair fuzzy environment. Thus, in this paper, we firstly propose some new point operators to make it conform to q-rung orthopair fuzzy numbers (q-ROFNs). Then, associated with classic arithmetic and geometric operators, we propose a new class of point weighted aggregation operators to aggregate q-rung orthopair fuzzy information. These proposed operators can redistribute the membership and non-membership in q-ROFNs according to different principle. Furthermore, based on these operators, a novel approach to multi-attribute decision making (MADM) in q-rung orthopair fuzzy context is introduced. Finally, we give a practical example to illustrate the applicability of the new approach. The experimental results show that the novel MADM method outperforms the existing MADM methods for dealing with MADM problems.
Multi-attribute decision making q-Rung orthopair fuzzy set q-Rung orthopair fuzzy point operators q-Rung orthopair fuzzy point weighted aggregation operators
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This work was partially supported by a key program of the National Natural Science Foundation of China (No. 71532002) and Fundamental Research Funds for the Central Universities (No. 2017YJS067) and Beijing Logistics Informatics Research Base.
Compliance with ethical standards
Conflict of interest
The authors declare that there are no conflicts of interest to this work.
This article does not contain any studies with human participants or animals performed by any of the authors.
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