Optimizing Warehouse Operations with Autonomous Mobile Robots

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 .