Robotic-based compact storage and retrieval systems provide high-density storage in distribution center and warehouse applications. In the system, items are stored in bins, and the bins are organized inside a three-dimensional grid. Robots move on top of the grid to retrieve and deliver bins. To retrieve a bin, a robot removes all bins above one by one with its gripper, called bin digging. The closer the target bin is to the top of the grid, the less digging is required to retrieve the bin. In this paper, we propose a policy to optimally arrange the bins in the grid while processing bin requests so that the most frequently accessed bins remain near the top of the grid. This improves the performance of the system and makes it responsive to changes in bin demand. Our solution approach identifies the optimal bin arrangement in the storage facility, initiates a transition to this optimal set-up, and subsequently ensures the ongoing maintenance of this arrangement for optimal performance. We perform extensive simulations on a custom-built discrete event model of the system. Our simulation results show that under the proposed policy more than half of the bins requested are located on top of the grid, reducing bin digging compared to existing policies. Compared to existing approaches, the proposed policy reduces the retrieval time of the requested bins by over 30% and the number of bin requests that exceed certain time thresholds by nearly 50%.

翻译：机器人密集仓储系统为配送中心和仓库应用提供了高密度存储方案。该系统中，物品存储于料箱内，料箱按三维网格结构排列。机器人沿网格顶部移动以取送料箱。取料时，机器人需使用夹爪逐一移除目标料箱上方的全部料箱（即"挖箱"操作）。目标料箱距离网格顶部越近，所需挖掘操作越少。本文提出一种最优料箱排列策略，在处理取箱请求时动态调整料箱布局，使高频访问料箱始终保持在网格近顶部区域。该方法既能提升系统性能，又可快速响应料箱需求变化。通过识别存储设施的最优料箱布局方案，该解决方案实现了向最优布局的迁移，并持续维护该布局以实现最优性能。我们基于自主构建的离散事件系统模型进行大规模仿真，结果表明：该策略下超过半数请求料箱位于网格顶部，有效减少了挖箱操作。与现有策略相比，本策略使请求料箱的取箱时间降低30%以上，超时阈值请求数量减少近50%。