Bilevel collaborative optimization for unmanned aerial vehicle logistics hub location and delivery routing

Existing unmanned aerial vehicle (UAV) logistics systems suffer from poor coupling between logistics hub locations and delivery routes and insufficient responsiveness to dynamic service demands. To address these issues, this paper proposes an optimization method based on a bilevel coupled model. The method jointly optimizes UAV logistics hub location selection and delivery route planning. In the lower level of the model, the adaptive large neighborhood search (ALNS) algorithm combined with the A* algorithm is employed to optimize the delivery path for a given logistics hub location. In the upper level of the model, a genetic algorithm (GA) is employed to optimize the hub location on the basis of the optimal path that is provided by the lower layer, together with the surrounding environmental conditions and the logistics hub construction costs. The scheme undergoes continuous iterative optimization via the dynamic coupling of the upper and lower layers. Experimental results demonstrate that the proposed method generates rational hub locations and effectively integrates the optimization of hub siting and transportation routing, achieving superior performance compared to baselines.