Maclaurin Symmetric Mean Aggregation Operators and Their Application to Hesitant Q-Rung Orthopair Fuzzy Multiple Attribute Decision Making

The Maclaurin symmetric mean(MSM) operator exhibits a desirable characteristic by effectively capturing the correlations among multiple input parameters, and it serves as an extension of certain existing aggregation operators through adjustments to the parameter k. The hesitant q-rung orthopair set(Hq-ROFSs) can serve as an extension of the existing orthopair fuzzy sets, which provides decision makers more freedom in describing their true opinions. The objective of this paper is to present an MSM operator to aggregate hesitant q-rung orthopair numbers and solve the multiple attribute decision making(MADM) problems in which the attribute values take the form of hesitant q-rung orthopair fuzzy sets(H-qROFSs). Firstly, the definition of H-qROFSs and some operational laws of H-qROFSs are proposed. Then we develop a family of hesitant q-rung orthopair fuzzy maclaurin symmetric mean aggregation operators, such as the hesitant q-rung orthopair fuzzy maclaurin symmetric mean(Hq-ROFMSM) operator, the hesitant q-rung orthopair fuzzy weighted maclaurin symmetric mean(Hq-ROFWMSM) operator, the hesitant q-rung orthopair fuzzy dual maclaurin symmetric mean(Hq-ROFDMSM) operator, the hesitant q-rung orthopair fuzzy weighted dual maclaurin symmetric mean(Hq-ROFWDMSM) operator. And the properties and special cases of these proposed operators are studied. Furthermore, an approach based on the Hq-ROFWMSM operator is proposed for multiple attribute decision making problems under hesitant q-rung orthopair fuzzy environment. Finally,a numerical example and comparative analysis is given to illustrate the application of the proposed approach.