协同无人机配送订单池化与路网监测：通过监测任务订单化

中文摘要 AI 辅助

本文研究了共享无人机机队用于按需食品配送和城市路网监测的实时调度问题。我们考虑了一种快递员-无人机协作场景，其中快递员将订单运送到发射台，无人机完成到售货亭的最后配送段。无人机可以在单次飞行中整合多个起讫点订单，并在满足配送时间约束的前提下，进行监测感知的路线调整以收集实时交通信息。这产生了一个联合决策问题，耦合了动态订单-无人机匹配、多订单池化、路径规划以及机队层面竞争和不确定性下的时变监测。我们提出了监测任务订单化方法，该方法定期将高拥堵和过时信息的路网节点转换为虚拟监测订单。将这些虚拟任务与食品配送订单池化，创建了一个统一的异构任务集，并将耦合的匹配与路径规划问题转化为订单级决策过程。基于这一抽象，我们构建了一个分散的图互依多智能体马尔可夫决策过程，并开发了图多智能体Q学习（Graph-MAQL），通过二分匹配协调图捕获局部智能体间依赖。然后，智能体-任务价值估计被用作动态异构二分匹配程序中的边权重，以实现全局可行的执行。使用真实世界数据的实验揭示了配送与监测之间的强大运营协同效应。监测任务订单化将监测性能提高了25.1%，而配送性能下降不到1%，同时Graph-MAQL将总体目标提高了最多20.8%，将截止时间违规减少了超过40%，并且无需重新训练即可零样本迁移到更高需求强度。

英文摘要

This paper investigates the real-time dispatch of a shared drone fleet for on-demand food delivery and urban road network monitoring. We consider a courier-drone collaborative setting in which couriers transport orders to launchpads and drones complete the final delivery leg to kiosks. Drones may consolidate multiple origin-destination orders within one flight and make monitoring-aware route adjustments to collect real-time traffic information subject to delivery-time constraints. This yields a joint decision problem coupling dynamic order-to-drone matching, multi-order pooling, routing, and time-varying monitoring under fleet-level competition and uncertainty. We propose monitoring-task orderization, which periodically converts road-network nodes with high congestion and stale information into virtual monitoring orders. Pooling these virtual tasks with food-delivery orders creates a unified heterogeneous task set and transforms the coupled matching-and-routing problem into an order-level decision process. Building on this abstraction, we formulate a decentralized graph-interdependent Multi-Agent Markov Decision Process and develop Graph Multi-Agent Q-Learning (Graph-MAQL), which captures localized inter-agent dependencies through bipartite match coordination graphs. Agent-task value estimates are then used as edge weights in a dynamic heterogeneous bipartite matching program for globally feasible execution. Experiments using real-world data reveal strong operational synergy between delivery and monitoring. Monitoring-task orderization improves monitoring performance by 25.1% with less than a 1% reduction in delivery performance, while Graph-MAQL improves the aggregate objective by up to 20.8%, reduces deadline violations by over 40%, and transfers zero-shot to higher demand intensity without retraining.

发表机构

• The Hong Kong University of Science and Technology（香港科技大学）
• École Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院（EPFL））

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