نگين بخشي

With the rapid expansion of the electric vehicle industry an‎d electronic equipment, the deman‎d for lithium-ion batteries has increased significantly. This surge in deman‎d has introduced new challenges in supply chain management. Consequently, designing an efficient supply chain for the production an‎d recycling of these batteries has become essential to mitigate environmental impacts an‎d support the transition toward a sustainable economy. In this regard, in the present study, an‎d with the aim of providing an innovative solution for the green management of the lithium-ion battery supply chain, a two-phase approach based on reinforcement learning an‎d mathematical modeling has been employed. In the first phase, using reinforcement learning, the most optimal raw-material supplier is selec‎ted taking into account market fluctuations an‎d existing uncertainties so as to ensure that raw materials are procured with the highest quality an‎d lowest cost. In the second phase, using linear mathematical modeling, a closed loop supply chain network is designed in which, in addition to reducing costs, environmental impacts are also minimized. Also, to accurately forecast deman‎d for this chain in future years, time series models have been employed. This combined approach allows the model to respond to market changes with greater flexibility an‎d to create a green an‎d efficient supply chain. To eva‎luate the effectiveness of the proposed model, a case study concerning lithium-ion batteries used in electric vehicles in Iran is conducted. The results demonstrate that the designed model can substantially reduce costs an‎d enhance the environmental sustainability of existing supply chain processes.

باتري‌هاي ليتيومي , زنجيره‌تامين حلقه‌بسته , پايداري اقتصادي و زيست‌محيطي , بهينه‌سازي دو هدفه , يادگيري تقويتي , پيش‌بيني تقاضا , سري‌هاي زماني

Lithium-ion batteries , Closed-loop supply chain , Economic an‎d environmental sustainability , Bi-objective optimization , Reinforcement learning , Deman‎d forecasting , Time series

Negin Bakhshi

Dr. Ehsan Dehghani