Multi-attribute group decision-making based on q-rung orthopair fuzzy Aczel–Alsina power aggregation operators

Abstract

This article aims to develop the Aczel–Alsina operational laws based on power aggregation operators (PAOs) for the q-Rung orthopair fuzzy set (FS) (q-ROFS) framework. Changing the parameter can alter these sets according to the degree of fluctuation, providing a range of options. The decision-making sciences use “q-ROFS” to describe the family of the most distinct fuzzy information thoughts. In response to the degree of variation, the q-ROFSs can increasingly adjust the information region by fluctuating the restriction q≥1, making numerous options. But on the other hand, PAOs have the advantage of vanishing the influence of awkward data from the final results. To take advantages of POAs, based on Aczel- Alsina (AA) operational laws, q-Rung orthopair fuzzy (q-ROF) AA power-weighted averaging (q-ROFAAPWA) and q-ROF AA power-weighted geometric (q-ROFAAPWG) operators are stated. Also, it studied that the proposed AOs fulfilled the conditions of boundedness, monotonicity, and idempotency. Based on these newly constructed AOs, a technique for dealing the multi-attribute group decision-making (MAGDM) problems is suggested. Numerous research, correlations with another modern approach and a numerical example of selecting stock market companies have been used to show the suggested system’s applicability. The sensitivity analysis of the developed method is examined. A comparative study with other prevailing methods is also providing for superiority analysis. Finally, we demonstrated that in the results and discussion section, the proposed AOs in the q-ROPFS framework are more reliable in aggregating information than intuitionistic fuzzy (IF) set (IFS) and Pythagorean FS (PyFS) frameworks.