Robust Design and Optimization of Integrated Sustainable Lithium-Ion Battery Supply Chain Network under Demand Uncertainty

Along with their widespread application, lithium-ion batteries (LIBs) have recently gained growing acceptance as a sustainable and clean technology. In this regard, the present study proposes a two-phase approach based on a new hybrid multicriteria decision-making (MCDM) method and sustainable robust optimization (RO) approach to design and plan a LIBs supply chain (SC) under uncertainty. In the first phase, the most appropriate possible locations for battery manufacturing zones are determined based on various ecological, social, and technical criteria. For this purpose, the Bayesian best-worst method (BBWM) and technique for order of preference by similarity to ideal solution (TOPSIS) are combined through the hybrid MCDM analysis. In the second phase, a two-stage stochastic RO model is formulated to optimize a set of strategic and tactical decisions for LIBs SC. The proposed LIBs SC design is a sustainable mixed-integer linear programming (MILP), multiproduct, and multiperiod model that addresses environmental, social, and economic objectives. Finally, the model’s performance is evaluated by conducting an actual case study in Iran, offering key managerial insights. Findings reveal that the hybrid BBWM-TOPSIS method reduces the optimization model’s computational complexity by eliminating 45% of inappropriate locations. Furthermore, raw material supply cost and manufacturing activities account for the highest portion of the total cost and EI elements by 53% and 60%, respectively.