Operational policies and performance analysis for overhead robotic compact warehousing systems with bin reshuffling
研究一种新型高架机器人紧凑仓储系统，提出嵌套排队网络模型，用强化学习优化料箱重排策略，发现可大幅减少重排距离和计算时间，相比AutoStore系统成本降低30%。
This paper studies a novel robotic warehousing system called the overhead robotic compact storage and retrieval system, which can free up the floor space occupation at a low cost. Bins, as basic storage containers, are stacked on top of each other to form a bin stack. Along overhead tracks, bin-picking robots transport bins between storage/retrieval positions and workstations with the aid of track-changing robots. Little research has been done to study operational policies and performance analysis for this new robotic compact warehousing system. We propose a nested queuing network model that considers two transportation resources and performs reinforcement learning using real data to improve the reshuffling efficiency. We find that reinforcement learning based reshuffling policy greatly reduces the reshuffling distance and saves computation time compared to existing policies. We find that the storage policy of stacks affects the optimal width/length ratio regardless of the system height. Interestingly, we obtain the number of robots that can stabilise the system to avoid an explosion of the order queue; two more robots than that number will produce relatively low throughput times. Compared to an AutoStore system, using our system reduces cost by 30% with a slight increase in throughput time.