Planning a parts-to-picker order picking system with consideration of the impact of perceived workload
Journal
Transportation Research Part E: Logistics and Transportation Review
Journal Volume
173
Date Issued
2023-05
Author(s)
Abstract
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.
Subjects
E-commerce | Order picking system | Parts-to-picker | Queueing theory | Robot-human coordination | Smart warehouses
Publisher
Elsevier
Type
journal article