Planning a parts-to-picker order picking system with consideration of the impact of perceived workload

In the digital age, coordinating robots and humans is critical in e-commerce warehousing. Motivated by observed industrial practices, this study presents a queueing theory based analytical model to investigate the planning issue of robot-human coordination in a parts-to-picker warehousing system. The critical planning decisions involve finding the optimal number of robots in the warehouse, the expected number of robots at essential locations of the warehouse, and performance analysis of the order picking system. A distinctive feature of this study is the conceptualization of a human factor called perceived workload (which depends on the number of robots) in the order picking planning model for efficient order fulfillment. Our analyses interestingly suggest that deploying more robots in warehouses with a parts-to-picker system does not necessarily increase the warehousing system's performance; instead, a trade-off exists. Additionally, the workload-dependent service rate significantly influences the robots queueing in front of the order picking station (internal queue) and the synchronization station (external queue) in the warehouse. More importantly, this work contributes to the design of a human-centric work environment for parts-to-picker order fulfillment system.

Automated summary

This study investigates the planning issue of robot-human coordination in e-commerce warehousing using a queueing theory based analytical model. The critical planning decisions include determining the optimal number of robots, the expected number of robots at essential locations, and analyzing the performance of the order picking system. The study introduces the concept of perceived workload, which depends on the number of robots, to design an efficient order fulfillment model. The findings suggest a trade-off between deploying more robots and the warehousing system's performance, as well as the significant impact of workload-dependent service rate on robot queueing in the warehouse.