A Dynamic Programming Model for Joint Optimization of Electric Drayage Truck Operations and Charging Stations Planning at Ports

The adoption of electric vehicles at ports is a promising approach to achieve sustainability goals. However, realizing the full potential of this strategy depends on effective coordination between infrastructure planning and operational scheduling. In this paper, we propose a joint optimization framework that can co-optimize these two components to minimize the overall system cost. To capture the dynamic nature of scheduling decisions, we model the problem using dynamic programming techniques. Our model accounts for the spatial and temporal heterogeneities of charging and driving costs for different truck trips. To evaluate the effectiveness of our proposed framework, we conducted an empirical study at the Port of Los Angeles and Port of Long Beach. Specifically, we aimed to fulfill 5% of the daily 20-foot equivalent unit containers using electric drayage trucks. Our model identified the optimal number of electric trucks, charging stations, and truck schedules required to meet the container throughput requirement. We also analyzed the cost per container as a function of daily throughput level for various scenarios. Our findings provide insights on how to determine charger supply based on daily throughputs at ports, and how to choose the appropriate ratios of electric trucks and battery sizes in the truck fleet under different throughput and electric price cases.