GREAT: A Graph Edge Attention Network for the Electric Vehicle Routing Problem with Asymmetric Costs

The paper introduces GREAT, a Graph Edge Attention Network that operates directly on edge space to handle asymmetric energy costs in the Electric Vehicle Routing Problem (EVRP). Integrated into a reinforcement learning framework, GREAT jointly optimizes routing and charging decisions, outperforming a strong HGS-based heuristic by up to 8.78% in energy savings and reducing runtime by a factor of 60. It also shows competitive performance against a custom adaptive large neighborhood search and achieves state‑of‑the‑art results on other asymmetric routing benchmarks such as TSP, CVRP, and Orienteering.

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• llms
• reinforcement-learning
• benchmarks