A novel approach on m-polar fuzzy set with Dombi power aggregation operators to solve multi-attribute decision-making problems

Abstract

This article introduces new aggregation operators by combining Dombi and power operators, resulting in m-polar fuzzy Dombi power averaging and geometric operators, with several key properties analyzed. A novel approach is then developed using these combined operators to assist in selecting the best company for stock market investments. Unlike existing research, which primarily utilizes homogeneous sub-characteristics for each attribute in the mPF environment, this study emphasizes the use of heterogeneous sub-characteristic collections in our application to address complex, uncertain decisionmaking challenges. Finally, the proposed approach is compared to various established operators and the MABAC method, with an analysis of its advantages and limitations.