发表机构
College of Management and Economics, Tianjin University; Laboratory of Computation and Analytics of Complex Management Systems (CACMS), Tianjin University（天津大学管理与经济学部; 天津大学复杂管理系统计算与分析实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对最后一英里取件操作中订单调度和路由决策复杂的问题，提出集成优化框架，结合路由预言机与实时调度启发式方法，开发相关网络编码器和解码器及调度启发式方法，实验表明该方法能有效支持物流公司解决此类问题。
AI 中文摘要
近年来，最后一英里取件操作日益复杂，这增加了物流平台快速准确决策的需求。此挑战主要由订单调度和路由这两个关键且紧密相关的决策过程驱动。分别解决它们会忽略其相互依存关系，而完全的端到端学习在大规模、可变规模实例上由于稀疏奖励可能不稳定且成本高。为解决此问题，我们提出一个集成优化框架，将学习到的路由预言机与实时调度启发式方法相结合。对于路由子问题，我们开发了一个带有前瞻快递员个性化解码器的动态残差图注意力网络编码器。对于调度子问题，我们开发了一种带有局部搜索的路由预言机引导的调度启发式方法，其中预言机提供接近最优的解决方案来选择候选快递员，同时保持实时可扩展性。我们使用来自菜鸟物流的真实世界数据集进行了广泛实验，包括离线评估和在线滚动时域模拟。实验结果表明，我们的方法在解决方案质量和求解时间方面优于其他基准，表明它可以有效地支持物流公司解决实时和大规模的最后一英里取件问题。
英文摘要
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.
