带时间窗口的卡车-无人机路线问题的分支价格-削减

Hong-qi Li, Feilong Wang
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引用次数: 4

摘要

考虑到无人机与卡车在最后一英里包裹递送中的重要现实效益,我们定义了带时间窗的卡车-无人机路线问题(TDRP - TW)。TDRP‐TW具有时间窗、途中同步、直接交付、多辆卡车和每辆卡车搭载多架无人机等特点。卡车路线覆盖的客户可以作为无人机的发射/检索地点,在本研究中称为卫星。途中的同步使无人机能够从卡车上发射,并在必要时返回到出发地以外的节点上的配对卡车上。我们提出了一种有效的分支价格和切割算法,其中引入了候选前向卫星(候选前向卫星)的概念来管理由于路径同步引起的标记挑战。此外,将分支价格切割算法与自适应大邻域搜索相结合,以获得大规模实例的近似解。在计算实验中,求解了具有多达50个客户的实例的最优性,并给出了具有100个客户的大规模实例的近似解。
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Branch‐price‐and‐cut for the truck–drone routing problem with time windows
Considering the important realistic benefits of drones combined with trucks for last‐mile parcel deliveries, we define the truck–drone routing problem with time windows (TDRP‐TW). The TDRP‐TW has the characteristics of time windows, synchronization en route, direct delivery, multiple trucks, and multiple drones carried by each truck. Customers covered by truck routes can be used as drone launch/retrieval locations, which are called satellites in this study. The synchronization en route enables drones to launch from trucks to return to paired trucks at nodes other than departure sites if necessary. We present an effective branch‐price‐and‐cut algorithm, in which a concept named candidate forward‐satellite (CFS) is introduced to manage the labeling challenge caused by the synchronization en route. In addition, the branch‐price‐and‐cut algorithm is combined with an adaptive large neighborhood search to obtain approximation solutions for large‐scale instances. In the computational experiments, instances with up to 50 customers are solved to optimality, and approximation solutions of large‐scale instances with 100 customers are presented.
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