Learning to Route Electric Trucks Under Operational Uncertainty

2026-04-29

AI Summary

This study addresses the path planning challenge for electric trucks under multiple operational uncertainties—including limited range, lengthy charging times, uncertain energy consumption, and competition for shared charging stations—by formulating it as an event-driven semi-Markov decision process with shared charging resources. The work proposes a novel integration of graph neural network–based state representation and a rule-based action masking mechanism, which significantly enhances reinforcement learning training efficiency while ensuring operational feasibility. A high-fidelity simulation environment is developed to support algorithm training and benchmark comparisons. Experimental results demonstrate that the proposed method consistently outperforms heuristic strategies across various fleet sizes, achieves performance close to the optimal benchmark, and maintains high task success rates even under charging congestion and high uncertainty scenarios.

Abstract

Electric truck operations require routing decisions that remain feasible under limited battery range, long charging times, travel and energy consumption, and competition for shared charging infrastructure. These features make electric truck routing a coupled logistics and energy problem, limiting the practicality of heuristics-based methods and rendering them computationally infeasible at scale. This paper proposes a learning-based framework for the stochastic electric truck routing under charging constraints and operational uncertainty. The problem, solved by Reinforcement Learning, is formulated as an event-driven semi-Markov decision process with shared charging resources, stochastic travel and energy requirements, and realistic nonlinear fast-charging behavior. To support learning in this setting, a graph-based representation of system state and feasible decisions is introduced, together with a rule-based action mask that restricts policies to operationally admissible actions; thus, improving training efficiency. Building on this formulation, an event-driven simulation environment is developed that supports both Reinforcement Learning and benchmarking against heuristic and mathematical programming baselines. Computational experiments across a range of fleet sizes show that the proposed learning-based algorithm consistently outperforms baselines and attains performance close to optimization benchmarks in many settings, while preserving high success rates under charging congestion and uncertainty.

Problem

• electric truck routing
• operational uncertainty
• charging constraints
• stochastic routing
• shared charging infrastructure

Innovation

• Reinforcement Learning
• Electric Truck Routing
• Operational Uncertainty
• Event-driven Simulation
• Action Masking