Delivery optimization for collaborative truck–drone routing problem considering vehicle obstacle avoidance

• Published in: Computers & industrial engineering Vol. 198; p. 110659
• Main Authors: Kong, Fanhui, Jiang, Bin
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2024
• Subjects: Collaborative truck–drone routing problem; Deep reinforcement learning; Logistics delivery optimization; Pointer network; Vehicle obstacle avoidance; Logistics delivery optimization; Deep reinforcement learning; Pointer network; Collaborative truck–drone routing problem; Vehicle obstacle avoidance
• ISSN: 0360-8352
• DOI: 10.1016/j.cie.2024.110659

Drone participating in logistics delivery has gained much concern due to its potential superiority of operational efficiency and service costs. How to optimize the hybrid vehicle delivery trip is still a crucial issue. Besides, complex urban environment and regulatory no-through zones bring more rigorous challenges. The aim is to develop an efficient routing solution that considers obstacles encountered by the truck and drone, ensuring the timely delivery. This paper proposes an efficient deep reinforcement learning to optimize the collaborative truck–drone routing problem (C-TDRP) under vehicle obstacle avoidance trail. Specifically, the trucks deploy a fleet of drones to serve the random distributed consumers subject to minimum delivery cost. Homogeneous drones serve multiple demand nodes per trip with limited energy carrying capacity. We design the C-TDRP formulation with studying the feasibility of obstacle avoidance routing. Owing to the computational complexity of the proposed mathematical model, an intelligent optimization algorithm based on deep reinforcement learning is developed, named pointer network (Ptr-Net), which can capture the position weights associated with network sequence automatically. Through rigorous experimental validations, the results demonstrate that the vehicle energy consumption of final trajectory reduces by more than 42%, even for large-scale delivery scenarios. Our proposed method not only enhances the optimization performance but also lays the foundation for more intelligent and adaptable logistics delivery in complex urban environments. •We construct a vehicle route optimization strategy for the coordination problem between one truck and many drones.•By using deep reinforcement learning algorithms to optimize sequence decision problems, the pointer network model is trained repeatedly through a multi-layer neural network structure.•By prioritizing obstacles and determining obstacle avoidance paths, the smooth delivery route of vehicles can be ensured.