Published 2024-12-10
Keywords
- Electrical Vehicles,
- Battery,
- Supply Chain,
- Optimization,
- Machine Learning
How to Cite
Abstract
This paper presents a dynamic optimization model for the electric vehicle (EV) battery supply chain, addressing the critical need for efficient logistics amidst fluctuating demand and market conditions. The growing adoption of EVs necessitates robust supply chain mechanisms to ensure timely and cost-effective battery delivery. This task is challenging due to the complexity of real-time adjustments required in logistics, considering factors such as transportation delays, battery availability, and cost constraints. We develop a model that dynamically adjusts supply chain logistics in real-time, leveraging advanced optimization techniques including reinforcement learning, stochastic programming, and robust optimization. We validate our approach through extensive experiments under various demand scenarios, demonstrating significant improvements in supply chain efficiency, cost savings, and battery delivery speed. Our results highlight the effectiveness of our dynamic optimization approach in managing the complexities of the EV battery supply chain.
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