卡车-无人机协同配送网络的系统级规划与协调
Systems-Level Planning and Coordination of Truck-Drone Collaborative Delivery Networks
AI总结：
针对城市最后一英里配送，提出分层规划与协调框架，通过任务编排与智能体同步，实现卡车-无人机协同配送，相比纯卡车模式，总配送时间减少42.4%，能耗降低44.2%。
摘要：
城市最后一英里包裹配送日益依赖异构车队，其性能取决于及时协调、可靠通信和可扩展控制。卡车-无人机协作已成为一种网络化信息物理配送范式，结合了卡车的载重能力和续航效率与无人机在拥挤或受限城市环境中的灵活性。本文从系统与控制角度提出了一种分层规划与协调框架，用于构建卡车-无人机协同配送（TDCD）。该框架由五个相互关联的层组成：空间需求对齐、协同配送配置、资源与工作流编排、性能评估和可扩展性分析，为网络化配送操作中的协调、控制和系统级性能提供了统一视角。使用源自2021年亚马逊最后一英里路线研究挑战数据集的实际城市最后一英里配送场景评估了所提框架。案例研究表明，通过结构化任务编排和智能体间同步实现的协调卡车-无人机操作，在操作约束下提高了端到端系统效率。结果显示，与传统的纯卡车配送模型相比，总配送时间减少了42.4%，能耗降低了44.2%。可扩展性分析进一步强调了协调收益如何随系统规模增大而持续，并展示了高效控制和通信在异构配送网络中的重要性。
英文摘要：
Urban last-mile parcel delivery increasingly relies on heterogeneous fleets whose performance depends on timely coordination, reliable communication, and scalable control. Truck-drone collaboration has emerged as a networked cyber-physical delivery paradigm that combines the payload capacity and range efficiency of trucks with the agility of drones in congested or access-limited urban environments. This paper proposes a layered planning and coordination framework that structures truck-drone collaborative delivery (TDCD) from a systems and control perspective. The framework consists of five interrelated layers: spatial-demand alignment, collaborative delivery configuration, resource and workflow orchestration, performance evaluation, and scalability analysis, providing a unified view of coordination, control, and system-level performance in networked delivery operations. The proposed framework is evaluated using a realistic urban last-mile delivery scenario derived from the 2021 Amazon Last Mile Routing Research Challenge dataset. The case study demonstrates how coordinated truck-drone operation, enabled by structured task orchestration and inter-agent synchronization, improves end-to-end system efficiency under operational constraints. Results show a 42.4% reduction in total delivery time and a 44.2% reduction in energy consumption compared to a conventional truck-only delivery model. The scalability analysis further highlights how coordination gains persist as system size increases, and shows the importance of efficient control and communication in heterogeneous delivery networks.
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