A multi-objective approach for designing a circular supply chain for electric vehicle batteries

This study presents a multi-objective, multi-period mixed integer programming model for designing a circular supply chain network for electric vehicle batteries (EVBs), crucial for the transition to sustainable transportation. With the goal of net zero emissions by 2053, EVBs must be efficiently remanufactured, reused, and recycled at their end-of-life stage. A Markov Chain-based approach is employed to model battery health transitions and uncertainties in these processes. The model optimizes economic, environmental, and social objectives. Social sustainability is assessed based on unemployment rates, migration due to job searches, poverty rate, income level and gender employment inequality, weighted using the entropy method . The AUGMECON2 method generates a Pareto optimal solution set, while Simple Additive Weighting (SAW) identifies the best solution. A case study of Türkiye validates the model, demonstrating how a circular EVB supply chain can minimize emissions, improve cost efficiency , and enhance social welfare. The findings provide decision-support insights for policymakers and industry leaders, emphasizing the importance of strategic facility planning, regulatory support, and sustainable investments in transitioning toward a circular economy . • Addresses gaps in CLSC design by focusing on broader social impacts like migration and gender inequality. • Integrates social factors into CLSC design for EV batteries using the entropy method. • Integrates Markov Chains into CLSC design to model battery health and guide reuse, remanufacturing, recycling decisions. • Integrates regulatory requirements like the EU mandate on minimum recycled content in battery components into CLSC design. • Multi-objective model balances economic, environmental, and social goals with the AUGMECON2 optimization method.