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Dombi power partitioned Heronian mean operators of q-rung orthopair fuzzy numbers for multiple attribute group decision making
Author(s) -
Yanru Zhong,
Hong Gao,
Xiuyan Guo,
Yuchu Qin,
Meifa Huang,
Xiaonan Luo
Publication year - 2019
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0222007
Subject(s) - operator (biology) , fuzzy logic , power (physics) , mathematical optimization , set (abstract data type) , computer science , mathematics , fuzzy set , group decision making , norm (philosophy) , group (periodic table) , artificial intelligence , physics , biochemistry , chemistry , repressor , quantum mechanics , transcription factor , political science , law , gene , programming language
In this paper, a set of Dombi power partitioned Heronian mean operators of q -rung orthopair fuzzy numbers ( q ROFNs) are presented, and a multiple attribute group decision making (MAGDM) method based on these operators is proposed. First, the operational rules of q ROFNs based on the Dombi t-conorm and t-norm are introduced. A q -rung orthopair fuzzy Dombi partitioned Heronian mean ( q ROFDPHM) operator and its weighted form are then established in accordance with these rules. To reduce the negative effect of unreasonable attribute values on the aggregation results of these operators, a q -rung orthopair fuzzy Dombi power partitioned Heronian mean operator and its weighted form are constructed by combining q ROFDPHM operator with the power average operator. A method to solve MAGDM problems based on q ROFNs and the constructed operators is designed. Finally, a practical example is described, and experiments and comparisons are performed to demonstrate the feasibility and effectiveness of the proposed method. The demonstration results show that the method is feasible, effective, and flexible; has satisfying expressiveness; and can consider all the interrelationships among different attributes and reduce the negative influence of biased attribute values.

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