Rollout-Based Charging Scheduling for Electric Truck Fleets in Large Transportation Networks

TL;DR

This paper presents a rollout-based dynamic programming method for optimizing charging schedules of large electric truck fleets, balancing cost efficiency and real-time adaptability.

Contribution

It introduces a novel two-layer rollout framework that efficiently decouples sequencing and charging decisions for large-scale fleet management.

Findings

Achieves near-optimal solutions with polynomial-time complexity.

Outperforms conventional heuristics in simulation studies.

Demonstrates effectiveness for real-time large-scale fleet charging.

Abstract

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.