Designing a sustainable-remanufacturing closed-loop supply chain under hybrid uncertainty: Cross-efficiency sorting multi-objective optimization

Abstract

Government legislations, resource scarcity, the recycling of end-of-life products and process development have spurred researchers to design the sustainable closed-loop supply chain as well as consider improved remanufacturing process. In this article, a multi-objective mixed-integer programming model is developed to configure and optimize a sustainable-remanufacturing closed-loop supply chain (SRCLSC) network. The model aims to reduce net costs, environmental degradation, and lost working days resulting from occupational accidents. Furthermore, a scenario-based fuzzy robust programming approach, which combines fuzzy robust programming and robust optimization, is developed to address the high hybrid uncertainty in the SRCLSC network. To solve the resulting model, a cross-efficiency sorting multi-objective optimization methodology that combines network optimization and performance evaluation is proposed. Data envelopment analysis is applied to evaluate the performance and cross-efficiency of feasible solutions. Performance evaluation results can guide the optimization process of multi-objective optimization algorithms. On the other hand, supply chain managers can determine the optimal solution based on the cross-efficiency values of solutions. The numerical case result verifies the effectiveness and applicability of the proposed model. In addition, it demonstrates that the proposed optimization method achieves the optimal solution based on the cross-efficiency of feasible solutions and has better solution performance than the conventional algorithm. Finally, sensitivity analyses and managerial insights are conducted.