Complex q-Rung Orthopair Fuzzy Aczel-Alsina Aggregation Operators and Its Application to Multiple Criteria Decision-Making With Unknown Weight Information

In decision making problems, complex q-rung orthopair fuzzy set is regarded as a more practical tool than complex intuitionistic fuzzy set and q-rung orthopair fuzzy set. This paper proposes several aggregation operators based on the Aczel-Alsina t-norm and t-conorm for aggregating complex q-rung orthopair fuzzy data. The suggested operators are then used to establish a multiple criteria decision-making (MCDM) method. The Aczel-Alsina operations t-norm and t-conorm can have the advantage of good flexibility with the operational parameter. In this regard, we expand the notions of the Aczel-Alsina t-norm and t-conorm to the complex q-rung orthopair fuzzy environment and provide certain aggregation operators in this study. Furthermore, we show the compatible features of the suggested operators. To overcome the defects of the existing entropy measures, a novel complex q-rung orthopair fuzzy entropy approach is put forward for acquiring the unknown criteria weights objectively. Following that, we describe an MCDM technique with unknown criterion weights in a complex q-rung orthopair fuzzy environment grounded on the originated operators. Then, to demonstrate the model's flexibility and validity, we analyze and solve a problem concerning with the selection of the sector that had the most impact on the Pakistan Stock Exchange. Subsequently, we demonstrate how the parameter's inclusion in our proposed model influences decision-making outcomes. At last, the generated outcomes are compared to the past approaches to demonstrate our suggested technique's superiority.

Automated summary

This paper proposes aggregation operators based on the Aczel-Alsina t-norm and t-conorm for complex q-rung orthopair fuzzy data and suggests a multiple criteria decision-making method. It also introduces a novel complex q-rung orthopair fuzzy entropy approach to acquire unknown criteria weights objectively. The flexibility and validity of the model are demonstrated in a problem related to the selection of a stock exchange sector, and the superiority of the proposed technique is compared to past approaches.