The development of 5G communication and Internet of Things technology has promoted the construction of smart airports. The testing and operation of unmanned baggage transport vehicles have reduced carbon emissions and operating costs. However, vehicle batteries have a limited lifespan. Therefore, this article establishes a baggage vehicle loading and unloading scheduling model that considers battery degradation costs from the perspective of discharge depth, and plans ahead to control discharge depth and reduce battery degradation. The model adopts a detachable transport mode for baggage vehicle and designs an improved genetic algorithm for solution. In the dynamic scheduling stage, the concept of timeline and critical points, as well as a custom insert initial point, are introduced to handle flight delays or advances. Based on pre-scheduling, a re-scheduling is carried out to process dynamic information in a timely manner, and an approximate algorithm is designed for fast solution. The effectiveness of the method was verified through simulation experiments.


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    Titel :

    Optimized scheduling of unmanned baggage transport vehicles in airports considering battery degradation


    Beteiligte:
    Sun, Yanan (Herausgeber:in) / Jiang, Bin (Herausgeber:in) / Han, Yafang (Autor:in) / Li, Mingxin (Autor:in) / Guo, Yibo (Autor:in)

    Kongress:

    Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023) ; 2023 ; Chongqing, China


    Erschienen in:

    Proc. SPIE ; 12923


    Erscheinungsdatum :

    08.11.2023





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch






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