Pythagorean fuzzy aczel-alsina power bonferroni mean operators for multi-attribute decision-making

Pythagorean fuzzy set (PyFS) is an effective model to describe the vagueness and uncertainty of decision-makers. Several novel averaging aggregation operators (AOs) have been formulated by using the PyF environment. The main focus of this article is to explore the unique mathematical model of Aczel-Alsina operational laws (A-AOls) for PyF information. To comprehensively identify the theory of power Bonferroni mean (PBM) operators which is the generalized formation of power average (PA) and Bonferroni mean (BM) operators that tend to reduce the adverse consequences of imprecise predictions and can perform the connections between attributed values. To acquire benefits from PA and BM operators, we utilize (A-AOls) and power Bonferroni AOs (PBAOs) to propose some new AOs such as PyFA-APBM and PyFA-A weighted PBM (PyFA-AWPBM) operators and also elaborate their persuasive particular characteristics. The proposed PyFAAWPBM operators are specifically relevant to improve the accuracy and adaptability of the information integration process by incorporating the AA operational rules Moreover, we provide a methodology for solving multi-attribute decision-making problems (MADM) by using the PyF framework based on the newly developed AOs. Furthermore, a numerical example is provided to demonstrate the efficiency and reliability of the developed AOs. Finally, a comparative analysis is carried out to exhibit the reliability and validity of the suggested strategy.