Rollout-Based Charging Scheduling for Electric Truck Fleets in Large Transportation Networks
This work addresses the operational cost minimization for electric truck fleets in transportation networks, representing an incremental improvement in scheduling methods.
The paper tackles the charging scheduling problem for large electric truck fleets by proposing a rollout-based dynamic programming framework that decouples ordering decisions from schedule optimization, achieving near-optimal solutions with polynomial-time complexity and adapting to dynamic conditions like arrivals and electricity prices.
In this paper, we investigate the charging scheduling optimization problem for large electric truck fleets operating with dedicated charging infrastructure. A central coordinator jointly determines the charging sequence and power allocation of each truck to minimize the total operational cost of the fleet. The problem is inherently combinatorial and nonlinear due to the coupling between discrete sequencing decisions and continuous charging control, rendering exact optimization intractable for real-time implementation. To address this challenge, we propose a rollout-based dynamic programming framework built upon an inner-outer two-layer structure, which decouples ordering decisions from the schedule optimization, thus enabling efficient policy evaluation and approximation. The proposed method achieves near-optimal solutions with polynomial-time complexity and adapts to dynamic arrivals and time-varying electricity prices. Simulation studies show that the rollout-based approach significantly outperforms conventional heuristics with high computational efficiency, demonstrating its effectiveness and practical applicability for real-time charging management in large-scale transportation networks.