Online Drone Scheduling for Last-mile Delivery
<|reference_start|>Online Drone Scheduling for Last-mile Delivery: Delivering a parcel from the distribution hub to the customer's doorstep is called the \textit{last-mile delivery} step in delivery logistics. In this paper, we study a hybrid {\it truck-drones} model for the last-mile delivery step, in which a truck moves on a predefined path carrying parcels and drones deliver the parcels. We define the \textsc{online drone scheduling} problem, where the truck moves in a predefined path, and the customer's requests appear online during the truck's movement. The objective is to schedule a drone associated with every request to minimize the number of drones used subject to the battery budget of the drones and compatibility of the schedules. We propose a 3-competitive deterministic algorithm using the next-fit strategy and 2.7-competitive algorithms using the first-fit strategy for the problem with $O(\log n)$ worst-case time complexity per request, where $n$ is the maximum number of active requests at any time. We also introduce \textsc{online variable-size drone scheduling} problem (OVDS). Here, we know all the customer's requests in advance; however, the drones with different battery capacities appear online. The objective is to schedule customers' requests for drones to minimize the number of drones used. We propose a $(2\alpha + 1)$-competitive algorithm for the OVDS problem with total running time $O(n \log n)$ for $n$ customer requests, where $\alpha$ is the ratio of the maximum battery capacity to the minimum battery capacity of the drones. Finally, we address how to generate intervals corresponding to each customer request when there are discrete stopping points on the truck's route, from where the drone can fly and meet with the truck.<|reference_end|>
@article{jana2024online,
    title={Online Drone Scheduling for Last-mile Delivery},
    author={Saswata Jana, Giuseppe F. Italiano, Manas Jyoti Kashyop, Athanasios L.
  Konstantinidis, Evangelos Kosinas and Partha Sarathi Mandal},
    journal={arXiv preprint arXiv:2402.16085},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.16085},
    primaryClass={cs.DS}
}