Research on Location-Routing Problem for Logistics UAVs Based on Bi-layer Programming

Drone logistics transportation is difficult to achieve long-distance delivery directly due to the short range of drones, and reasonable vertiport location and air-route planning have become key issues that need to be urgently addressed. This study proposes a bi-layer optimization approach to address the problems of vertiport location and air-route planning, investigating the interaction between facility placement and route design. Considering the variations in coverage demand, the proposed methodology incorporates a hierarchical vertiport construction strategy and establishes a multi-tier delivery system utilizing heterogeneous drone fleets. The improved GA and GACO are used to solve the site selection and path planning models, respectively. The benchmark and case study results show that the improved GA and GACO algorithm are better than the original algorithm in terms of solution quality and coverage in the same scenario and benchmark. The improved GACO algorithm provide better average results, higher stability, and effectively reducing flight time and path costs. The proposed algorithms achieve a total cost reduction of 8.96%. The results further verify that bi-layer programming approach maintains excellent cost optimization performance and network adaptability even as the number of demand points increases. In addition, the hierarchical vertiport construction strategy generates 6.51%-11.65% cost savings compared to conventional non-hierarchical approaches. The proposed delivery network exhibits strong robustness and has good adaptability to the influence of data noise. This study not only fills a theoretical gap in the cooperative planning of vertiports and routes, but also provides critical technical support for the scalable implementation of drone logistics, offering significant theoretical value and practical engineering implications.