Drones have received growing attention in logistics recently. One possible application is deploying drones for auditing inventory in warehouses. With the use of drones, warehouses are able to increase inventory record accuracy and decrease labor costs. In this research, we introduce the stocktaking drone routing problem (STDRP), which consists of routing a fleet of drones through a warehouse for stocktaking purposes as well as deciding on the location of charging stations on the warehouse floor, which is necessary due to the limited battery capacity of the drones. Subsequently, we develop an adaptive large neighborhood search-based heuristic (ALNS) with novel solution encoding and decoding approaches to solve the STDRP. In a numerical study, we show that ALNS can solve realistic instances in reasonable time. We also derive recommendations regarding the ideal size of the drone fleet, the charging infrastructure, and battery capacity. Finally, we investigate the interplay between the storage assignment policy (such as the popular ABC rule) and stocktaking efficiency using drones.

中文翻译：

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定位充电站和路由无人机，以实现高效的自动盘点

无人机最近在物流领域受到越来越多的关注。一种可能的应用是部署无人机来审计仓库中的库存。通过使用无人机，仓库能够提高库存记录的准确性并降低劳动力成本。在这项研究中，我们介绍了盘点无人机路由问题（STDRP），该问题包括为盘点目的在仓库中路由无人机机队以及决定仓库地板上充电站的位置，这是必要的，因为无人机的电池容量有限。随后，我们开发了一种自适应大邻域搜索启发式（ALNS），采用新颖的解决方案编码和解码方法来解决 STDRP。在数值研究中，我们表明 ALNS 可以在合理的时间内解决现实实例。我们还就无人机机队的理想规模、充电基础设施和电池容量提出建议。最后，我们研究了存储分配策略（例如流行的 ABC 规则）和无人机盘点效率之间的相互作用。