用深度强化学习整合快递派单与路线规划，提升末端取件效率。
Integrated Order Dispatching and Routing for Last-Mile Pickup via Deep Reinforcement Learning
- 将派单与路径规划联合优化，利用路由模型指导实时调度。
- 在菜鸟真实数据集上，解的质量和求解速度均优于基准方法。
- 适合需要实时处理大规模末端取件任务的物流平台使用。
近年来，末端取件操作日益复杂，对物流平台快速精准决策的需求不断提升。这一挑战源于两个紧密耦合的核心决策过程：订单派送与路径规划。分步求解会忽略两者关联性，而完全端到端学习在大规模、可变规模实例上因奖励稀疏导致不稳定且成本高。为此，我们提出一种集成优化框架，将学习得到的路由代理与实时调度启发式结合。针对路径子问题，设计了动态残差图注意力网络编码器与前瞻式骑手个性化解码器；针对派单子问题，提出基于路由代理引导的启发式调度算法，利用代理提供近似最优解以筛选候选骑手，同时保持实时可扩展性。在菜鸟物流的真实数据集上进行大量实验，包括离线评估与在线滚动窗口仿真。结果表明，该方法在解的质量和求解时间上均优于其他基线，证明其能有效支持物流企业应对实时、大规模的末端取件问题。
原文摘要 · Abstract (English)
In recent years, the growing complexity of last-mile pickup operations has increased the need for fast and accurate decision-making on logistics platforms. This challenge is fundamentally driven by two key and tightly coupled decision-making processes: order dispatching and routing. Solving them separately overlooks their interdependence, while fully end-to-end learning can be unstable and costly on large, variable-scale instances due to sparse rewards. To solve this problem, we propose an integrated optimization framework which couples a learned routing oracle with real-time dispatching heuristics. For the routing subproblem, we develop a Dynamic-Residual Graph Attention Network encoder with a Look-Ahead Courier-Personalized decoder. For the dispatching subproblem, we develop a routing-oracle-guided dispatching heuristic with local search, where the oracle provides near-optimal solutions to select candidate couriers while retaining real-time scalability. Extensive experiments on real-world datasets from Cainiao Logistics are used to test the performance of our approach, including an offline evaluation and an online rolling-horizon simulation. The experimental results show that our approach outperforms other benchmarks regarding solution quality and solving time, indicating it can effectively support logistics companies in solving real-time and large-scale last-mile pickup problems.
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