Dual Hesitant q-Rung Orthopair Fuzzy Muirhead Mean Operators in Multiple Attribute Decision Making

On account of the indeterminacy and subjectivity of decision makers (DMs) in complexity decision-making environments, the evaluation information over alternatives presented by DMs is usually fuzzy and ambiguous. As the generalization of intuitionistic fuzzy sets (IFSs) and Pythagorean fuzzy sets (PFSs), the q-rung orthopair fuzzy sets (q-ROFSs) are more useful to express more fuzzy and ambiguous information. Meanwhile, to consider human's hesitance, the dual hesitant q-rung orthopair fuzzy sets (DHq-ROFSs) are presented which can be more valid of handling real MADM problems. To fuse the information in DHq-ROFSs more effectively, in this article, some Muirhead mean (MM) operators based on DHq-ROFSs environment, which consider any number of being fused arguments, are defined and studied. Evidently, the new proposed operators can obtain more exact results than other existing methods. In addition, some precious properties of these MM operators are discussed and all the special cases of them are investigated which indicates MM operator is more powerful than others. Afterward, the defined aggregation operators are used to solve the MADM with dual hesitant q-rung orthopair fuzzy numbers (DHq-ROFNs) and the MADM decision-making model is developed. In accordance of the defined operators and built model, two operators are applied to deal with the MADM problems for supplier selection with the DHPFNs information and the availability and superiority of the proposed operators are analyzed by comparing with some existing approaches. The method presented in this paper can effectually solve the MADM problems in which the decision-making information is expressed by the DHq-ROFNs and the attributes are interactive.