Optimizing Warehouse Operations with Autonomous Mobile Robots

Lu Zhen (), Zheyi Tan (), René de Koster (), Xueting He (), Shuaian Wang () and Huiwen Wang ()
Additional contact information
Lu Zhen: School of Management, Shanghai University, Shanghai 200444, China
Zheyi Tan: School of Management, Shanghai University, Shanghai 200444, China
René de Koster: Rotterdam School of Management, Erasmus University, 3062 PA Rotterdam, Netherlands
Xueting He: School of Management, Shanghai University, Shanghai 200444, China
Shuaian Wang: Faculty of Business, The Hong Kong Polytechnic University, Kowloon, Hong Kong
Huiwen Wang: School of Management, Shanghai University, Shanghai 200444, China

Transportation Science, 2025, vol. 59, issue 5, 1130-1152

Abstract: Autonomous mobile robots (AMRs) can support human pickers in warehouse picking operations by reducing picker walking distance and increasing the warehouse’s throughput. AMR-assisted order picking is becoming popular as it can be conveniently implemented in conventional warehouses. This study proposes an integrated optimization model for scheduling the operations in AMR-assisted picker-to-parts warehouse systems. The model aims to minimize the makespan of all picking operations for a batch of orders by assigning batched orders to AMRs, selecting storage racks for AMRs and pickers to visit, and determining the routes of the AMRs and the pickers. A column- and row-generation algorithm is designed to solve the model using synchronization constraints between AMRs and pickers. Numerical experiments are conducted to validate the applicability of our proposed algorithm in a warehouse that handles 16,000 orders per day. Our algorithm can solve small-scale instances to optimality. Our algorithm can also obtain better solutions in less time than a column generation (CG)–based method. Extensive experiments are conducted to derive managerial insights.

Keywords: warehouse intralogistics; autonomous mobile robots; collaborative robots; picker-to-parts systems; column-and-row generation (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://dx.doi.org/10.1287/trsc.2024.0800 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ortrsc:v:59:y:2025:i:5:p:1130-1152

Access Statistics for this article

More articles in Transportation Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().