Developing a Robust Multi-objective Optimization Model for Reverse Logistics of Electric Vehicle Batteries

In response to the damaging impacts of greenhouse gas emissions, Electric Vehicles (EVs) have emerged as a sustainable alternative. However, the rise of EVs creates the challenge of disposing numerous retired batteries. Hence, designing and optimizing Reverse Logistics (RL) for Electric Vehicles Batteries (EVBs) can be an effective approach. Existing uncertainties, however, cause a gap between the outputs of exact optimization models and real-world conditions. Addressing this, the present study develops a robust multi-objective optimization model that incorporates uncertainty such as the return rates of retired batteries. Making it more applicable to real-world scenarios. This research compares the relative performances of deterministic and the proposed robust optimization models and validates the model by calculating violation probabilities within a robust optimization framework. This validation is conducted by allocating a specific budget for robustness to ensure that the model remains effective under various uncertain conditions. Additionally, this study explores adjusting the level of conservatism in decision-making by applying the price of robustness approach to manage conservatism in our decision-making process. Highlighting the importance of innovative sustainable practices, this work offers a practical route for stakeholders to collaboratively mitigate the challenges associated with the end-of-life management of EV batteries, thus contributing to environmental sustainability and resource efficiency in the BEV industry.