<ul><li>Researchers propose a reinforcement learning-based adaptive variable neighborhood search method for the Vehicle Routing Problem with Multiple Time Windows (VRPMTW).</li><li>The method integrates reinforcement learning to dynamically select neighborhood operators based on real-time solution states and learned experience.</li><li>A transformer-based neural policy network is used for intelligently guiding operator selection during local search.</li><li>Experiments show that RL-AVNS outperforms traditional methods, achieving significant improvements in solution quality and computational efficiency across various scenarios.</li></ul>

Learning to Search for Vehicle Routing with Multiple Time Windows

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