Multi-Attribute Group Decision Making Method Based on EDAS Under Picture Fuzzy Environment

Multi-attribute group decision making (MAGDM) is one of the most important research hotspots in the field of decision sciences. Many practical problems are often characterized by MAGDM. The aim of this paper is to develop a new approach for MAGDM problems, in which the attribute values take the form...

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Bibliographic Details
Published in:IEEE access 2019, Vol.7, p.141179-141192
Main Authors: Li, Xia, Ju, Yanbing, Ju, Dawei, Zhang, Wenkai, Dong, Peiwu, Wang, Aihua
Format: Article
Language:English
Subjects:
Clustering methods
Decision making
dice similarity measure
Economics
Evaluation
Evaluation based on distance from average solution (EDAS)
Fuzzy logic
Fuzzy sets
Handheld computers
maximizing deviation method
multi-attribute group decision making (MAGDM)
Operators
Optimization
picture fuzzy interaction aggregation operators
picture fuzzy numbers (PFNs)
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Summary:Multi-attribute group decision making (MAGDM) is one of the most important research hotspots in the field of decision sciences. Many practical problems are often characterized by MAGDM. The aim of this paper is to develop a new approach for MAGDM problems, in which the attribute values take the form of picture fuzzy information, and the information about the weights of attributes and decision makers is unknown. Firstly, some picture fuzzy interaction operators are presented, such as the picture fuzzy weighted interaction averaging (PFWIA) operator, picture fuzzy ordered weighted interaction averaging (PFOWIA) operator and picture fuzzy hybrid ordered weighted interaction averaging (PFHOWIA) operator. In the meantime, some desirable properties of these operators are discussed in detail. Secondly, to get reasonable decision result, we propose a method to determine the weights of decision makers under picture fuzzy setting based on the idea of the Dice similarity measure. Thirdly, for the situations where the information about the attribute weights is partly known, we establish an optimization model to determine the attribute weights on the basis of the maximizing deviation method. Fourthly, we propose a new method to solve MAGDM problems by extending the traditional Evaluation based on Distance from Average Solution (EDAS) method. Finally, an illustrative example is given to demonstrate the calculation process of the proposed method, and the method is verified by comparing the evaluation result with that of two existing methods.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2943348