Learning to Search for Vehicle Routing with Multiple Time Windows

Researchers developed RL-AVNS, a reinforcement learning-enhanced adaptive variable neighborhood search method for solving the Vehicle Routing Problem with Multiple Time Windows, outperforming traditional heuristics in solution quality and efficiency. The approach uses a transformer-based neural policy network to dynamically select neighborhood operators, demonstrating strong generalization to unseen instances and practical applicability in complex logistics scenarios.