As health-conscious consumerism grows, logistics companies are rapidly developing low-altitude drone networks for the first-mile delivery of fresh agricultural products. To tackle the challenges associated with high operational expenses and fluctuating service quality, this study introduces a multi-batch capacitated drone routing optimization model designed to simultaneously minimize operational costs and maintain product freshness. Utilizing the Adaptive Large Neighborhood Search (ALNS) algorithm and benchmarking against exact solutions from the Gurobi solver, we demonstrate notable reductions in operational costs and enhancements in product freshness. Validated with real-world data from Yajiang County’s drone network, our research highlights the essential role of sophisticated routing strategies in improving service quality within logistics for perishable goods.


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    Titel :

    Optimizing first-mile drone routing for agricultural product pickups


    Beteiligte:
    Feng, Zhengang (Herausgeber:in) / Mikusova, Miroslava (Herausgeber:in) / Su, Tingting (Autor:in)

    Kongress:

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


    Erschienen in:

    Proc. SPIE ; 13575


    Erscheinungsdatum :

    28.04.2025





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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