Locker-based Truck-Drone Routing with Integrated Considerations of Pickups, Deliveries, and No-Fly Zones
This paper introduces a locker-based truck-drone routing problem (LTDRP-PDNF) that integrates pickups, deliveries, battery constraints, and no-fly zones, aiming to minimize total operational cost. The authors propose a two-stage deep reinforcement learning heuristic that outperforms baselines with short computation times.
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[Submitted on 26 Jun 2026]
Title:Locker-based Truck-Drone Routing with Integrated Considerations of Pickups, Deliveries, and No-Fly Zones
View a PDF of the paper titled Locker-based Truck-Drone Routing with Integrated Considerations of Pickups, Deliveries, and No-Fly Zones, by Xuanyu Liu and Hui Hu and Jiao Zhao and Ziliang Wang and Zhengbing He
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Abstract: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.
Subjects:
Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Physics and Society (physics.soc-ph)
Cite as: arXiv:2606.30680 [cs.RO]
(or arXiv:2606.30680v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.30680
arXiv-issued DOI via DataCite
Submission history
From: Zhengbing He [view email] [v1] Fri, 26 Jun 2026 20:20:07 UTC (2,818 KB)
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