Urban areas present unique challenges for drone-based deliveries due to the spatially and temporally variable nature of the wind field at low altitudes. In this paper, we propose a novel approach to address these challenges and ensure safe and reliable drone delivery routings in windy urban environments. Our approach incorporates the stochastic and spatially heterogeneous urban wind field using scenario-based stochastic programming and probabilistic modeling techniques. We develop stochastic mixed integer linear programs (SMILPs) that integrate realistic wind scenarios generated via large-eddy simulations (LESs) with the stochastic drone routing problem (SDRP). We employ the simulated annealing (SA) algorithm, which effectively explores the solution space and handles hundreds of customers' requests in a short time. We compare the performance of SA implementation with SMILPs implementation on small cases and show its superiority in terms of solution quality. Through simulations and performance evaluations on large-scale instances, we demonstrate the effectiveness of our approach under different wind conditions and number of customers. We also conduct a sensitivity analysis to investigate the influence of operational altitude (i.e., the height of the wind field) on the performance of our method. Our results provide valuable insights for optimizing drone routing decisions in windy urban environments.


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    Titel :

    Drone Delivery Routing with Stochastic Urban Wind


    Beteiligte:
    Chen, Minghao (Autor:in) / Smyth, Andrew W. (Autor:in) / Giometto, Marco G. (Autor:in) / Li, Max Z. (Autor:in)


    Erscheinungsdatum :

    24.09.2023


    Format / Umfang :

    3786523 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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