Adaptive airspace allocation model for urban drone logistics using multi-objective optimization under uncertainty.

The large-scale application of urban unmanned aerial vehicle (UAV) logistics is confronted with challenges such as limited airspace resources, dynamic changes in demand, and multiple uncertainties. Traditional static allocation methods are difficult to adapt to complex urban environments. This study constructs a DRL-RO hybrid framework that integrates deep reinforcement learning and discrete robust optimization. It characterizes the influence mechanisms of demand fluctuations, weather changes, and emergencies through a three-layer uncertainty modeling system, and introduces a policy network enhanced by an attention mechanism to capture spatio-temporal correlations in the spatial domain. The improved MOEA/D-DRL algorithm is adopted to achieve rapid approximation of the Pareto frontier. The verification of the actual scenarios in Shenzhen shows that this framework reduces the computational complexity to the sub-quadratic level while maintaining a high success rate. Through the hierarchical airspace management strategy, it effectively balances the three goals of distribution efficiency, flight safety and operating costs. The Wasserstein sphere constraint ensures robustness and scalability in extreme scenarios. It provides theoretical support and technical solutions for the construction of city-level unmanned aerial vehicle (UAV) traffic management systems.