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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 (Formula: see textROF) sets and enhanced with Aczel-Alsina-based prioritized aggregation operators. Specifically, we develop two base operators-the Formula: see textROF AA prioritized average (Formula: see textROFAAPA) and the Formula: see textROF AA prioritized geometric (Formula: see textROFAAPG)-along with their weighted prioritized counterparts, the Formula: see textROF AA prioritized weighted average (Formula: see textROFAAPWA) and the Formula: see textROF AA prioritized weighted geometric (Formula: see textROFAAPWG). 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 (Formula: see text), Cost (Formula: see text), Service level (Formula: see text), and Production capacity (Formula: see text)-Cost (Formula: see text) received the highest weight (0.2789), and supplier Formula: see text 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.
Ali et al. (Sat,) studied this question.