An Optimization Model for Reverse Logistics of Electric Vehicle Batteries

Currently, the trend toward sustainable development is a top priority in the economic and social agendas of countries worldwide. This global commitment is clearly reflected in the rapid adoption and expansion of electric vehicles, which are widely regarded as a key solution to reducing greenhouse gas emissions and minimizing dependence on fossil fuels. However, the accelerated deployment of EVs has also led to new challenges, particularly the scarcity of critical metals such as lithium, nickel, and cobalt essential elements in the production of lithium-ion batteries. It points out the need for efficient and sustainable systems for battery recovery, recycling, and reuse. This study addresses this challenge by proposing an optimization model for the design and operation of a reverse logistics system dedicated to electric vehicle battery repair, recovery, and recycling. The model integrates three fundamental decision-making dimensions: firstly, the optimal location of battery repair centers; secondly, the selection and placement of recovery and recycling facilities; and the final one is the determination of inventory levels and transportation quantities between all nodes in the system. The model is formulated as a Mixed-Integer Linear Programming (MILP) problem and is optimally solved using CPLEX and Excel. In addition to minimizing total costs including transportation, inventory, and facility opening costs the model explicitly incorporates environmental objectives by reducing carbon emissions from logistics activities and processing technologies. Moreover, although the model is developed for electric vehicle batteries, it can be generalized to other types of electronic waste to support broader circular economy initiatives. The results offer practical implications for supply chain managers, policymakers, and sustainability advocates in designing greener and more resilient reverse logistics networks.