Operational policies and performance analysis for overhead robotic compact warehousing systems with bin reshuffling

ABSTRACT

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.

KEYWORDS:

• Facility logistics
• robotic warehouse
• performance evaluation
• queuing
• machine learning

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available on request from the corresponding author.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [grant number 72372088] and Shenzhen Science and Technology Program [grant number GJHZ20220913143003006].

Notes on contributors

Rong Wang

Rong Wang is the doctor candidate majoring in Management Science and Engineering at Shenzhen International Graduate School and Department of Industrial Engineering, Tsinghua University, China. Her research field is automated warehousing system, and warehouse scheduling and optimisation.

Peng Yang

Peng Yang is an associate professor in the Division of Logistics and Transportation and Institute of Data and Information at Shenzhen International Graduate School, Tsinghua University, China. His research interests include order picking, warehouse operations, automated warehousing system, and green logistics facility.

Yeming Gong

Yeming (Yale) Gong is Head of AIM Institute, Director of Business Intelligence Center (BIC), and a full professor at Emlyon Business School. His 120+ articles are published or accepted in Web of Science journals including IJPR, MIS Quarterly, Production and Operations Management, Transportation Science, various IEEE Transactions and ACM Transactions.

Cheng Chen

Cheng Chen works as a logistics analyst in the public sector. He received the master’s degree in Logistics Engineering from Shenzhen International Graduate School and Department of Industrial Engineering, Tsinghua University.