Prioritized Aczel–Alsina aggregation operators under p, q-quasirung orthopair fuzzy environment for sustainable supplier selection in new energy vehicle industry

Abstract

Selecting sustainable suppliers in the new energy vehicle industry is a complex decision-making problem due to diverse criteria, uncertainty in evaluations, and the need to prioritize certain factors. Addressing this gap, we propose a novel multi-criteria decision-making (MCDM) framework based on p, q-quasirung orthopair fuzzy (ROF) sets and enhanced with Aczel–Alsina–based prioritized aggregation operators. Specifically, we develop two base operators—the ROF AA prioritized average (ROFAAPA) and the ROF AA prioritized geometric (ROFAAPG)—along with their weighted prioritized counterparts, the ROF AA prioritized weighted average (ROFAAPWA) and the ROF AA prioritized weighted geometric (ROFAAPWG). The mathematical properties of these operators are established, and an MCDM algorithm is formulated to incorporate decision-makers’ priority structures. The framework also integrates a mathematical formulation to objectively determine criteria weights, ensuring a balanced combination of subjective and data-driven inputs. A case study for a leading new energy vehicle manufacturer demonstrates the framework’s effectiveness: among four evaluation criteria—Quality (), Cost (), Service level (), and Production capacity ()—Cost () received the highest weight (0.2789), and supplier emerged as the most sustainable choice. Comparative experiments against established MCDM techniques confirm the proposed approach’s superior ranking stability and robustness. These results provide both a methodological advance for fuzzy decision-making research and a practical decision-support tool for industries pursuing environmentally responsible supply chain strategies.