The study addresses the siting and layout problem of unmanned aerial vehicle (UAV) vertiport in urban logistics scenarios. A multi-objective integer programming model is developed, incorporating multiple levels of facilities and multi-model drone delivery strategies. The model considers constraints such as airspace restrictions, UAV performance limitations, and vertiport conditions, with the objectives of minimizing total cost and maximizing customer satisfaction. To solve the model effectively, an improved NSGA-II algorithm is proposed. The algorithm integrates a reverse learning strategy to enhance the quality of the initial population and employs a dynamic crowding distance calculation to improve algorithm performance. Based on geographic information from Shanghai and logistics distribution data from express delivery companies, simulation experiments are conducted to validate the effectiveness of the model and algorithm. The results demonstrate that the proposed model and algorithm are effective. The multi-model UAV delivery strategy significantly optimizes both logistics costs and customer satisfaction compared to single-model strategies. Additionally, incorporating reverse learning and dynamic crowding distance in the NSGA-II algorithm markedly enhances its rootfinding ratio and success ratio. In conclusion, the vertiport location selection is an essential part of the early stages of UAV logistics development. This paper has achieved success in the simulated experiment which provides valuable insights and references for achieving low-cost and high-efficiency low-altitude logistics


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    Title :

    Urban logistics UAV vertiport location and layout planning method


    Contributors:

    Conference:

    International Conference on Smart Transportation and City Engineering (STCE 2024) ; 2024 ; Chongqing, China


    Published in:

    Proc. SPIE ; 13575


    Publication date :

    2025-04-28





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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