A Dual-Layer Optimization Framework for Multi-UAV Delivery Scheduling in Multi-Altitude Urban Airspace

Efficient UAV logistics in complex urban airspaces requires a synergistic approach to task allocation and path planning. However, traditional methods often decouple these two phases, leading to physically infeasible or sub-optimal delivery schedules. This paper proposes a Dual-Layer Optimization Framework (D-LOF) to address the Multi-UAV delivery problem in 3D urban environments. The upper layer utilizes an improved Genetic Algorithm (GA) with a specialized constraint repair operator to optimize task sequences for a heterogeneous UAV fleet. The lower layer employs an altitude-aware A* algorithm that dynamically balances vertical energy costs and horizontal cruise efficiency across multiple altitude layers. Unlike conventional models, our framework iteratively feeds precise 3D flight costs from the lower layer back to the upper layer to guide evolutionary search. Simulation results demonstrate that the D-LOF consistently achieves global convergence within 20 generations. Compared to single-altitude planning and rule-based strategies, the proposed method can reduce total operational costs and maintains zero time-window violations in high-density obstacle scenarios. This study provides a robust decision-making tool for “last-mile” urban logistics by navigating the trade-offs between 3D spatial constraints and delivery punctuality.