基于储物柜的卡车-无人机联合配送路径规划：集成考虑取货、送货与禁飞区
Locker-based Truck-Drone Routing with Integrated Considerations of Pickups, Deliveries, and No-Fly Zones
AI总结：
针对储物柜式卡车-无人机配送中取货、送货及禁飞区约束，提出两阶段深度强化学习神经启发式方法，以最小化总运营成本。
摘要：
卡车-无人机配送是一种新兴的最后一公里物流模式，结合了卡车的长途运输能力和无人机的灵活服务能力。在基于储物柜的运营中，智能储物柜不仅作为临时包裹存储设施，还作为自动化无人机停靠和服务节点。这些自动化节点支持无人机起飞、降落、包裹交接和电池更换，从而显著扩展了无人机辅助配送网络的服务范围和运营灵活性。然而，实际的储物柜式配送系统面临复杂的现实挑战，需要综合协调包裹配送、退货取件、电池受限和负载依赖的无人机飞行，以及绕开受限空域的必要绕行。为解决这一实际且多方面的挑战，本文引入了一个基于储物柜的卡车-无人机联合配送路径规划问题，综合考虑取货、送货和禁飞区（LTDRP-PDNF），目标是最小化配备无人机的卡车车队的总运营成本。我们将路径构建过程建模为马尔可夫决策过程，并开发了一种基于两阶段深度强化学习的神经启发式方法。第一阶段利用基于注意力的编码器和双向门控循环单元解码器来解决仅卡车的路径规划问题，该问题被建模为带容量约束的车辆路径问题。第二阶段结合策略迁移策略和混合调度分配启发式方法，为LTDRP-PDNF构建完全协调的卡车和无人机路线。在不同规模实例上的实验表明，所提方法在大多数情况下优于元启发式和神经启发式基线，同时保持极短的计算时间，为实际运营约束下提供了一种有效、可扩展的解决方案框架。
英文摘要：
Truck-drone delivery is an emerging last-mile logistics mode combining the long-haul capacity of trucks with the flexible service capability of drones. In locker-based operations, smart lockers serve not only as temporary parcel storage facilities but also as automated drone docking and service nodes. These automated nodes support drone takeoff, landing, parcel handover, and battery replacement, thereby significantly extending the service range and operational flexibility of drone-assisted delivery networks. However, practical locker-based delivery systems face complex real-world challenges, requiring the integrated coordination of not only parcel delivery, return pickup, battery-constrained and load-dependent drone flights, but also necessary detours around restricted airspace. To address this practical and multifaceted challenge, this paper introduces a locker-based truck-drone routing problem with integrated considerations of pickups, deliveries, and no-fly zones (LTDRP-PDNF), with the objective of minimizing the total operational cost of a fleet of drone-equipped trucks. We formulate the route construction process as a Markov Decision Process and develop a two-stage deep reinforcement learning-based neural heuristic. The first stage utilizes an attention-based encoder and a Bidirectional Gated Recurrent Unit decoder to solve the truck-only routing problem, formulated as a capacitated vehicle routing problem. The second stage combines a policy-transfer strategy with a hybrid dispatch assignment heuristic to construct fully coordinated truck and drone routes for LTDRP-PDNF. Experiments on instances of different scales demonstrate that the proposed method outperforms metaheuristic and neural heuristic baselines in most cases while maintaining exceptionally short computation times, offering an effective, scalable solution framework under practical operational constraints.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
