# Research on Demand-Based Scheduling Scheme of Urban Low-Altitude Logistics UAVs
###### Abstract  
Aiming at the problem of the scheduling scheme of urban low-altitude logistics unmanned aerial vehicles (UAVs), this paper establishes a demand-based UAV scheduling scheme model using an improved simulated annealing algorithm, taking minimizing the cost of distribution as the objective function and considering restrictions such as UAV performance constraints, airspace constraints, and distribution constraints, among others. For verification, actual express data and airspace constraints in Shanghai are taken as examples. Two urban air traffic networks are constructed using road and building data. The analysis results show that the planning scheme of this model is superior to other forecasting models in terms of delivery cost and delivery time. In addition, this model can flexibly calculate the optimal scheduling scheme under the constraints of multiple parameters, according to the requirements of delivery volume, delivery distance, UAV performance, etc. air transportation; urban air mobility; simulated annealing algorithm; UAV logistics; scheduling scheme +
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values and then determined the demand for logistics drones [7]. [PERSON] et al. proposed a robust solution with regard to the uncertainty in battery availability and consumption [8]. [PERSON] et al. developed a modeling framework to analyze drone delivery reliability under stochastic demand and meteorological conditions [9]. [PERSON] et al. considered the traveling salesman problem, in which trucks and drones cooperate to deliver packages to customers [10]. [PERSON] et al. further reduced flight delays by optimizing the sequence of drones performing conflict resolution [11]. [PERSON] et al. propose a two-level, multi-product, and multi-cycle integrated inventory-transportation planning model for carbon emissions [12] and another model for locating product collection centers considering risk and environmental factors [13]. [PERSON] et al. proposed a finite element scheme to numerically solve the flow equilibrium and calculate the system performance and investigated two specific test scenarios for last-mile freight delivery systems [14]. At present, there is relatively little research on drone demand in China. Based on the strategic background of civil-military integration, [PERSON] et al. considered the heterogeneity of demand points and distribution centers as well as the vulnerability of the network when investigating the location problem of drone distribution centers. Aiming at the different needs of different types of demand points for distribution centers, they established a drone distribution center model with the minimum total network mileage as the objective function [15]. [PERSON] proposed a distribution mode of \"vehicle + drone\" for rural e-commerce logistics. In order to solve the problem of its path optimization, he built a variety of distribution-path optimization models, using genetic algorithms and other algorithms to solve the models [16]. [PERSON] established a multi-stage drone-demand forecasting model with the objective function of maximizing express transportation volume and minimizing safety costs [17]. In terms of research on logistics node location model algorithms, there are mainly two categories: precise algorithms and heuristic algorithms. An accurate algorithm refers to a method that can directly calculate the optimal solution of a model. The more commonly accurate algorithms include the branch-and-bound method, the enumeration method, and the dynamic programming method, etc. Among them, the branch-and-bound method is the most commonly used method by scholars. [PERSON] [18] used the branch-and-bound method to solve the problem of node location without capacity constraints. [PERSON] [19] used an improved principle combined with the branch-and-bound method to calculate the location problem without capacity constraints based on previous studies. [PERSON] [20] studied the location problem of logistics facilities with capacity constraints using precise algorithms.