关键词: 航空运输, 起降点布局规划, 人类学习优化算法, 物流无人机, 物流配送

Abstract: This paper focuses on the layout planning of urban logistics Unmanned Aerial Vehicle (UAV) vertiports. In consideration of different types of logistics UAV vertiports, this paper proposes a vertiports layout planning model with the objective of minimizing the total economic cost and maximizing the customer satisfaction. The constraints of the model involve no-fly zone, UAV performance, vertiport capacity, and other factors. The human learning optimization algorithm (HLO) is designed and the random learning operator, individual learning operator and social learning operator are introduced in the algorithm to solve the model. The simulation experiment is then performed with real geographic data and logistics data to verify the effectiveness of the model and algorithm. The experimental results show that the proposed model can generate reasonable layout planning of vertiports, which is suitable and effective for large-scale resource allocation problem. The HLO algorithm shows better solution accuracy and convergence speed than the genetic algorithm (GA) The parameter analysis shows that the optimal economic cost weight is 0.4 and the optimal customer satisfaction weight is 0.6 based on the simulation environment. The optimal algorithm learning probability parameters are 5/n and 0.8+2/n. The study results could provide decision-making support for the layout planning of the actual urban logistics UAV vertiports.

Key words: air transportation, layout planning of vertiports, human learning optimization algorithm, logistics Unmanned Aerial Vehicle (UAV), logistics distribution