Optimizing Warehouse Operations with Autonomous Mobile Robots

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. Funding: This research was supported by the National Natural Science Foundation of China [Grants 72025103, 72394360, 72394362, 72401179, 72361137001, and 72371221], the Project of Science and Technology Commission of Shanghai Municipality China [Grant 23JC1402200], and the Research Grants Council of the Hong Kong Special Administrative Region, China (Project number HKSAR RGC TRS T32-707/22-N). Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2024.0800 .