Bi-Level Optimization for Integrated Vertiport Location and Air-Route Planning in Urban UAV Logistics Networks

Abstract

Urban drone logistics face significant challenges in long-distance delivery due to limited drone endurance, making reasonable vertiport siting and air-route planning a critical research problem. Existing studies typically treat facility siting and route configuration as independent or sequential decisions, leading to suboptimal network efficiency and empty-haul wastage. This study proposes an integrated bi-layer optimization framework that integrates vertiport location and air-route planning for urban UAV logistics networks. The upper-level model optimizes vertiport placement and tiered capacity sizing using an improved genetic algorithm (GA) with Halton sequence initialization and a memory bank mechanism, while the lower-level path planning model designs delivery routes via a Greedy-Embedded Ant Colony Optimization (GACO) algorithm. Unlike conventional approaches, the proposed framework establishes a closed-loop iterative coordination mechanism that feeds routing costs back into siting decisions, introduces a hierarchical vertiport deployment scheme dynamically matching facility capacity with regional demand density, and implements a semi-open routing policy that allows cross-station landings to reduce empty-haul flights. Numerical experiments based on real operational data from Shanghai demonstrate that the proposed framework achieves an 8.75% total cost reduction over the traditional sequential location-then-routing strategy. In addition, the hierarchical vertiport construction strategy contributes 4.49% cost savings compared to non-hierarchical schemes, while maintaining strong robustness, with facility overcapacity strictly below 5% under routine demand disturbances up to 10%. These results confirm the effectiveness of the proposed framework in improving the economic efficiency and operational adaptability of urban UAV delivery networks.