Learning to Route Electric Trucks Under Operational Uncertainty

TL;DR

This paper introduces a reinforcement learning framework for electric truck routing that accounts for operational uncertainties and charging constraints, outperforming traditional heuristics and optimization methods.

Contribution

It formulates electric truck routing as an event-driven semi-Markov decision process and develops a graph-based, rule-based approach to improve learning efficiency and scalability.

Findings

The learning-based algorithm outperforms heuristic baselines in various fleet scenarios.

It achieves near-optimal performance in many cases while maintaining high success rates under uncertainty.

The framework effectively handles shared charging infrastructure and nonlinear fast-charging behavior.

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.