Optimizing performance evaluation of green supply chain management practices with linguistic q-rung orthopair fuzzy information

Green supply chain management (GSCM) and environmentally conscious manufacturing have emerged as critical strategies for companies to enhance operational efficiency, reduce environmental impact, and improve profitability and market competitiveness. To address the inherent fuzziness and uncertainty in selecting optimal GSCM practices, this paper proposes a novel decision framework by integrating Criteria Importance Through Intercriteria Correlation (CRITIC) and Evaluation based on Distance from Average Solution (EDAS) methods under the linguistic q-rung orthopair fuzzy (Lq-ROF) environment. To support this integration, we define Hamacher operations for Lq-ROF numbers and develop two Hamacher aggregation operators. Then, the Lq-ROF-CRITIC-EDAS approach is designed to solve multi-criteria group decision-making problems by using these proposed aggregation operators. Moreover, the relative weights of the evaluation criteria for GSCM practices were determined using the Lq-ROF-CRITIC model. A real-world decision problem of evaluating the performance of GSCM practices is solved to verify our suggested approach. In addition, the sensitivity analysis is also carried out by changing the parameter’s value to check the consistency of the ranking order. Finally, the proposed model is compared with existing approaches to demonstrate its strength. The sensitivity and comparative analyses reveal that the suggested technique offers greater feasibility, reliability, and precision in ranking alternatives, outperforming traditional methods in handling uncertainty and aligning with real-world GSCM requirements.