Multi-Objective Task Scheduling for Vehicle–UAV Synchronous Cooperative Distribution Network Inspection

To address the challenges of significant vehicle parking constraints, limited UAV endurance, and insufficient multi-task coordination efficiency in distribution network inspection, this paper proposes a vehicle–UAV synchronous cooperative inspection task scheduling method based on multi-objective twin delayed deep deterministic policy gradient and nondominated sorting genetic algorithm II (MOTD3-NSGA-II). First, a vehicle–UAV synchronous cooperative inspection model is established by considering staged vehicle repositioning, same-site UAV launch, landing, and retrieval, as well as state-of-charge constraints. On this basis, a multi-objective optimization model is formulated with task coverage, mission completion time, minimum residual state of charge, and load balance as objectives. Then, a bi-level closed-loop solution framework is developed, in which NSGA-II is employed to optimize cooperative parameters and objective preference weights, while the inner-layer MOTD3 learns UAV scheduling policies in a continuous action space. Finally, the proposed method is validated in four simulation scenarios with different task scales and spatial distribution characteristics. The results show that 100% task coverage is achieved in all four scenarios, with mission completion times of 11,109 s, 9693 s, 10,538 s, and 10,721 s, respectively, while the minimum residual state of charge is maintained within 0.28–0.36. The results demonstrate that the proposed method can balance inspection completeness, execution efficiency, energy safety, and cooperative stability, providing a useful reference for intelligent task scheduling in vehicle–UAV cooperative distribution network inspection.