Multiple-Attribute Group Decision-Making Based on q-Rung Orthopair Fuzzy Power Maclaurin Symmetric Mean Operators

To be able to describe more complex fuzzy uncertainty information effectively, the concept of ${q}$ -rung orthopair fuzzy sets ( ${q}$ -ROFSs) was first proposed by Yager. The ${q}$ -ROFSs can dynamically adjust the range of indication of decision information by changing a parameter ${q}$ based on the different hesitation degree from the decision-makers, where ${q} {\ge } {1}$ , so they outperform the traditional intuitionistic fuzzy sets and Pythagorean fuzzy sets. In real decision-making problems, there is often an interaction phenomenon between attributes. For aggregating these complex fuzzy information, the Maclaurin symmetric mean (MSM) operator is more superior by considering interrelationships among attributes. In addition, the power average (PA) operator can reduce the effects of extreme evaluating data from some experts with prejudice. In this paper, we introduce the PA operator and the MSM operator based on ${q}$ -rung orthopair fuzzy numbers ( ${q}$ -ROFNs). Then, we put forward the ${q}$ -rung orthopair fuzzy power MSM ( ${q}$ -ROFPMSM) operator and the ${q}$ -rung orthopair fuzzy power weighed MSM ( ${q}$ -ROFPWMSM) operator of ${q}$ -ROFNs and present some of their properties. Finally, we present a novel multiple-attribute group decision-making (MAGDM) method based on the ${q}$ -ROFPWA and the ${q}$ -ROFPWMSM operators. The experimental results show that the novel MAGDM method outperforms the existing MAGDM methods for dealing with MAGDM problems.