An electric vehicle battery recycling and remanufacturing supply chain network design with sustainability and robustness under demand uncertainty

With the acceptance of new energy vehicles, there is a disconnect between the rapid growth of electric vehicles and the nascent phase of the battery recycling industry. In response to sustainable development, this paper presents a multi-objective model integrating economic, environmental, and social dimensions to design a sustainable closed-loop supply chain network for the electric vehicle battery industry. In addition, a resilient strategy is applied, and a Fuzzy Robust Stochastic model incorporating Conditional Value at Risk (FRS-CVaR) is proposed to enhance the overall resilience and robustness of the supply chain network. To solve the proposed model, an improved NSGA-II is developed by applying an initialization strategy to enhance the quality of initial solutions, incorporating an adaptive evolutionary strategy to accelerate convergence while maintaining population diversity, and use elite retention strategy to avoid inferior regions. The deterministic model and the FRS-CVaR model are solved using Gurobi, NSGA-II, and the improved NSGA-II, respectively. The improved NSGA-II algorithm achieves a 40 % and 22 % increase in the Hypervolume (HV) metric, while Spacing values are reduced by 27 % and 52 %, respectively, compared to NSGA-II. The results demonstrate that the improved NSGA-II converges faster and exhibits superior performance. Study findings show that the proposed model can be applied as an efficient tool for designing a sustainable, robust supply chain network based on decision makers' preferences. The results and analysis conclude with management insights on balancing robustness and sustainability in the supply chain network and adjusting the supply chain for products with different recycling rates and material recovery rates or future prospects.