Truck–UAV dynamic collaborative delivery route optimization

To address the challenges of high costs and low efficiency in last-mile logistics, this study investigates a truck–drone collaborative routing problem that incorporates dynamic rendezvous points and time synchronization. An integer programming model is formulated with the objective of minimizing travel and waiting costs, while a dynamic rendezvous mechanism is introduced. A two-level interactive optimization framework is proposed: in the outer layer, a Genetic Algorithm (GA) determines the truck routes; in the inner layer, an improved Dung Beetle Optimization (DBO) algorithm generates the drone delivery paths, enabling efficient spatiotemporal coordination between trucks and drones. Simulation results demonstrate that the proposed method significantly reduces total system costs compared with single-truck delivery and conventional static collaboration. Across test instances of different scales, the dynamic coordination strategy achieves an average cost reduction of about 25% over static strategies, with more pronounced advantages in scenarios featuring widely dispersed and large customer sets. Applicable to e-commerce logistics, emergency delivery, and remote last-mile distribution, this approach helps logistics companies optimize resources and reduce costs.