 This paper proposes a demand-based UAV scheduling scheme for urban low-altitude logistics operations. It uses an improved simulated annealing algorithm to optimize the delivery cost and delivery time while accounting for various constraints such as UAV performance, airspace constraints, and distribution constraints. To verify its effectiveness, two urban air traffic networks were constructed using real-world data from Shanghai. The results showed that the proposed model outperformed other forecasting models in terms of delivery cost and delivery time. Additionally, it was able to flexibly calculate the optimal scheduling scheme under different parameters such as delivery volume, delivery distance, and UAV performance. This article was authored by Honghai Zhang, Shershin Wu, Ujfeng, and others.