Q-rung orthopair fuzzy Aczel–Alsina aggregation operators with multi-criteria decision-making

Abstract

q-rung orthopair fuzzy sets (q-ROFSs) are advantageous for accurately expressing the preferences of decision-makers (DMs) due to their membership and non-membership degrees. This paper presents new q-rung orthopair fuzzy aggregation operators (AOs) that are based on Aczel–Alsina (AA) operations. These operators offer several advantages when dealing with real-world problems. The paper introduces new q-ROFS operations, such as the Aczel–Alsina product, sum, exponent, and scalar multiplication. We developed many AOs namely, the “q-rung orthopair fuzzy Aczel–Alsina weighted averaging (q-ROFAAWA) operator”, “q-rung orthopair fuzzy Aczel–Alsina ordered weighted averaging (q-ROFAAOWA) operator”, “q-rung orthopair fuzzy Aczel–Alsina hybrid averaging (q-ROFAAHA) operator”, “q-rung orthopair fuzzy Aczel–Alsina weighted geometric (q-ROFAAWG) operator,” the “q-rung orthopair fuzzy Aczel–Alsina ordered weighted geometric (q-ROFAAOWG) operator”, and the “q-rung orthopair fuzzy Aczel–Alsina hybrid geometric (q-ROFAAHG) operator”. Various attributes of these operators have been defined, including monotonicity, boundary, idempotency and commutativity. The paper demonstrates these properties for the suggested AOs. An algorithm for multi-criteria decision-making has been developed using the proposed aggregation operators with multiple evaluations by DMs and partial weight information under q-ROFSs. To demonstrate the effectiveness of the proposed approach, the paper uses a scenario for selecting the best green supplier. Additionally, the paper provides sensitivity analysis and compares the proposed technique with existing approaches.