mlsanityData to 14 Oct 2026

Learning to Route Electric Trucks Under Operational Uncertainty

arXiv 2604.26566 ↗First posted 29 Apr 2026eess.SY cs.LG cs.SYcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 13 of its 46 references that are in the map. First posted on arXiv 29 Apr 2026.

Builds on

13 of 46 references.

References · 13

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
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  3. 2018Attention, Learn to Solve Routing Problems!cited by 656
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  8. 2020Deep Reinforcement Learning for Electric Vehicle Routing Problem with Time Windowscited by 25
  9. 2024EV2Gym: A Flexible V2G Simulator for EV Smart Charging Research and Benchmarkingcited by 8
  10. 2018Heteroscedastic Gaussian processes for uncertainty modeling in large-scale crowdsourced traffic datacited by 6
  11. 2025Towards Generalization of Graph Neural Networks for AC Optimal Power Flowcited by 6
  12. 2023Toward Efficient Transportation Electrification of Heavy-Duty Trucks: Joint Scheduling of Truck Routing and Chargingcited by 2
1 more
  1. 2018Modelling the Time-dependent VRP through Open Datacited by 1

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  1. 2026A Curriculum-Based Deep Reinforcement Learning Framework for the Electric Vehicle Routing Problem
  2. 2024Safety-Aware Reinforcement Learning for Electric Vehicle Charging Station Management in Distribution Network
  3. 2026A Distributionally Robust Reinforcement Learning Framework for Constrained Urban EV Dispatch
  4. 2025Reinforcement Learning-based Approach for Vehicle-to-Building Charging with Heterogeneous Agents and Long Term Rewards
  5. 2025Optimizing Electric Vehicle Charging Station Placement Using Reinforcement Learning and Agent-Based Simulations
  6. 2024Electric Vehicle Routing Problem for Emergency Power Supply: Towards Telecom Base Station Relief
  7. 2018Definition and evaluation of model-free coordination of electrical vehicle charging with reinforcement learning
  8. 2025Reinforcement Learning for Hybrid Charging Stations Planning and Operation Considering Fixed and Mobile Chargers