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 ( \(^{pq}\) ROF) sets and enhanced with Aczel–Alsina–based prioritized aggregation operators. Specifically, we develop two base operators—the \(^{pq}\) ROF AA prioritized average ( \(^{pq}\) ROFAAPA) and the \(^{pq}\) ROF AA prioritized geometric ( \(^{pq}\) ROFAAPG)—along with their weighted prioritized counterparts, the \(^{pq}\) ROF AA prioritized weighted average ( \(^{pq}\) ROFAAPWA) and the \(^{pq}\) ROF AA prioritized weighted geometric ( \(^{pq}\) 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 ( \(\kappa _1\) ), Cost ( \(\kappa _2\) ), Service level ( \(\kappa _3\) ), and Production capacity ( \(\kappa _4\) )—Cost ( \(\kappa _2\) ) received the highest weight (0.2789), and supplier \({V_3}\) 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.
