# Research on Demand-Based Scheduling Scheme of Urban Low-Altitude Logistics UAVs
###### Abstract
Heuristic algorithms can relatively optimize the solving steps and also shorten the solving time, so many scholars use heuristic algorithms to solve models and obtain satisfactory solutions. Heuristic algorithms include the simulated annealing algorithm, the particle-swarm optimization algorithm, the Lagrange algorithm, and the genetic algorithm. [PERSON] and [PERSON] [21] used a simulated annealing algorithm to solve node-layout problems related to multiple time periods. [PERSON] and [PERSON] [22] used a simulated annealing algorithm to calculate the logistics network-layout model in multi-stage situations. The idea of particle-swarm optimization is similar to that of the simulated annealing algorithm. It is to set a random initial solution and then, through multiple iterations and fitness evaluation, track the currently searched optimal solution to find the global optimal solution of the model. [PERSON] [23] designed an improved particle-swarm optimization algorithm to solve the location model of distribution centers and demonstrated through examples that the improved particle-swarm optimization algorithm can significantly improve the model's solving speed and accuracy. In terms of logistics-drone-scheduling research, [PERSON] et al. [24] combined logistics drones with public transportation systems to form a heterogeneous multi-agent system. By solving the vehicle routing problem (VRP), they found a path for each package, thus finding a path for cost optimization under the conditions of a given heterogeneous multi-agent system and minimizing the number of drones required for delivery. [PERSON] et al. [25] used small autonomous drone systems (UAS) located below 500 feet in large urban areas as the analysis object and used data analysis and comparative analysis methods to predict the demand for logistics drones in the United States in 2050 and the economic benefits of using drones for package delivery. [PERSON] et al. [5] took PrimeAir in Amazon, Phoenix, Arizona, as an example to give a linear programming and simulation model for transporting packages. In this paper, two models are established. The first is a linear programming model that aims to maximize the revenue of operators and predicts the demand for logistics drones on this basis. The second is a simulation model that describes the impact of changes in consumer demand on logistics drone distribution. The simulation model is based on the output of the linear programming model. [PERSON] et al. [26] studied the lower bound of delivery time and infrastructure expenditure for goods transported from warehouses to customers through drones and proposed a strategy to minimize the overall workload. This strategy can be expanded based on the number of warehouses and drones, with optimal performance at low loads and normal operation at high loads. The simulation shows that if the number of drones in each time period is sufficient, it can meet the work needs of any load. To sum up, part of the studies only conducted research on demand forecasting methods and failed to reflect the application of drones as an emerging mode of transportation in logistics. Some studies only partially covered the requirements of drones and did not regard specific requirements as necessary constraints, while others only considered a single element, which is difficult to apply to actual delivery scenarios. In other words, the existing studies fail to take into account the performance of drones and the distribution environment of urban low-altitude logistics, which makes the demand-based scheduling results lack certain credibility. In order to enrich the research content of drone scheduling schemes, this paper learns from the ideas of the literature [17; 27], considers drone performance constraints such as load and endurance, and also considers the constraints of airspace conditions to establish a simulation model [28] for drone demand-based scheduling schemes.