Deploying pickers and robots in cobot-based collaborative order picking systems
As a promising application of cobots in labor-intensive warehouses, human–robot collaborative order picking systems provide a flexible and human-friendly picking solution by capitalizing on the best attributes of human pickers and robots. Few studies have determined operation modes of human–robot collaborative order picking systems to be beneficial to efficiency, cost, and the well-being of human workers. We identify four human–robot collaborative modes for order picking: single robot to single picker (couple), single robot to multiple pickers (SR-to-MP), single picker to multiple robots (SP-to-MR), and multiple pickers to multiple robots. For each mode, we establish a fork-join queuing network (FJQN) model to analyze system performance and apply a fatigue-recovery model to estimate the fatigue of the pickers. The proposed FJQN and fatigue-recovery model are validated by simulation. Although the throughput time and picker fatigue in the SR-to-MP mode can benefit from an appropriate zoning policy, we find, interestingly, that the zoning policy cannot reduce the throughput time in the SP-to-MR mode. The SP-to-MR mode is economical if a warehouse does not pursue a swift throughput time. A well-capitalized warehouse can adopt the SR-to-MP mode to improve the throughput time further in a more human-friendly manner.<br>
Authors
- Yeming Gong (ORCID: https://orcid.org/0000-0001-9270-5507)
- Zuo‐Jun Max Shen (ORCID: https://orcid.org/0000-0003-4538-8312)
- Shanshan Song (ORCID: https://orcid.org/0000-0003-3313-3232)
- Peng Yang (ORCID: https://orcid.org/0000-0002-6780-2847)
- Li Huang (ORCID: https://orcid.org/0000-0001-9559-7724)
Institutions
- University Town of Shenzhen (CN)
- Tsinghua–Berkeley Shenzhen Institute (CN)
- Artificial Intelligence in Medicine (Canada) (CA)
- École de management de Lyon (FR)
- University of Hong Kong (HK)
- Tsinghua University (CN)
Publication Details
- Journal
- IISE Transactions
- Published
- 2025-05-07
- DOI
- https://doi.org/10.1080/24725854.2025.2501036
- Citations
- 3
- Primary Topic
- Advanced Manufacturing and Logistics Optimization
- Type
- article
- Field-Weighted Citation Impact
- 3.73