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Learning to search for vehicle routing with multiple time windows
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DOI：10.1016/j.cie.2025.111760.png)
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Abstract
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• A novel RL-AVNS approach integrates reinforcement learning with adaptive variable neighborhood search for solving VRPMTW. • A specialized fitness metric quantifying customers’ temporal flexibility enhances the shaking phase effectiveness. • Computational experiments on realistic unmanned vending machine replenishment scenarios demonstrate RL-AVNS’s superior performance. • The approach exhibits strong generalization capabilities to unseen problem instances, offering practical value for complex logistics optimization.
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