Maclaurin symmetric mean operators and their applications in the environment of complex q-rung orthopair fuzzy sets

Abstract

The Maclaurin symmetric mean (MSM) and dual Maclaurin symmetric mean (DMSM) operators are two aggregation operators to aggregate the q-rung orthopair fuzzy numbers (q-ROFNs) into a single element. The complex q-rung orthopair fuzzy framework is more effective to describe fuzzy information in real decision-making problems. The complex q-rung orthopair fuzzy sets (Cq-ROFSs) are more superior to the complex intuitionistic fuzzy sets (CIFSs) and complex pythagorean fuzzy sets (CPFSs) to cope with uncertain and difficult information in the environment of fuzzy set theory. Thus, this manuscript proposes some complex q-rung orthopair fuzzy Maclaurin symmetric mean (Cq-ROFMSM), complex q-rung orthopair fuzzy weighted Maclaurin symmetric mean (Cq-ROFWMSM), complex q-rung orthopair fuzzy dual Maclaurin symmetric mean (Cq-ROFDMSM) and complex q-rung orthopair fuzzy weighted dual Maclaurin symmetric mean (Cq-ROFWDMSM) operators. Moreover, some properties and special cases of our proposed methods are also introduced. Then we present a multi-attributive group decision-making based on proposed methods. Further, a numerical example is provided to illustrate the flexibility and accuracy of the proposed operators. Last, the proposed methods are compared with existing methods to examine the best emerging technology enterprises.