Last mile delivery (LMD) refers to the transfer of goods from the final dispatch point to a transportation centre. This is the most difficult and expensive component of the logistics chain for many logistics disciplines. New technologies enable new methods of information collection, distribution, and provision of logistical services. For instance, the enhanced drone technology of the present day, with its now-acceptable payload, autonomy, and collision avoidance capabilities, can facilitate logistical solutions. This strategy has the potential to reduce last-mile logistics costs and contribute to environmental objectives. The demand for LMD services can be divided into two categories: planned demand and unexpected demand. The purpose of this study is to develop a hybrid solution for LMD. The solution consists of ground vehicles for planned demand and unmanned aerial vehicles for unplanned demand. The ultimate objective of this study is to estimate the generated UAV traffic in urban areas by employing a worldwide continuous technique for sizing both fleets. The ground LMD routing and scheduling activities are globally analysed using an empirical formula that relates the lowest cost route operation in a congested metropolitan region to the mean length required to service a customer, while the UAV LMD activity is globally analysed using a stochastic model. As a consequence of this LMD solution, this study generates estimates of traffic intensity, enabling the computation of performance indices related to service quality and environmental impact.


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    Titel :

    Assessing Drone-Based Last-Mile Logistics—A Hybrid Solution


    Weitere Titelangaben:

    Lect.Notes Mechanical Engineering


    Beteiligte:

    Kongress:

    International Seminar on Aeronautics and Energy ; 2022 September 17, 2022 - September 17, 2022



    Erscheinungsdatum :

    19.11.2023


    Format / Umfang :

    11 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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