Designing a circular supply chain network for electric vehicle batteries: An improved robust possibilistic programming approach

The global transition to electric vehicles heightens the need for sustainable strategies to manage lithium-ion batteries, particularly at the End-of-Life (EoL) stage. Traditional reverse and closed-loop supply chains often fail to cope with uncertainties in battery returns, quality variability, and weak consumer engagement. This study introduces a novel circular supply chain network model in which Customer Relationship Management (CRM) strategies are incorporated as decision-related mechanisms to increase return rates of EoL batteries. A bi-objective Robust Possibilistic Programming (RPP) framework is developed to address inherent epistemic uncertainty in demands, return behaviors, and system's parameters while yielding a robust network design. To overcome the model's nonlinearity, an improved RPP approach is devised, enabling tractable optimization through solving linear counterparts. The AUGMECON method is used to explore trade-off analysis between economic and social objectives. A real-world case study validates the model's effectiveness, showing improvements in material recovery, return efficiency, and reduced dependence on virgin resources. The results confirm that CRM-based consumer engagement and robust modeling jointly enhance operational performance and environmental outcomes.