Improving Throughput in Multi-Robot Warehousing via Matching-Theoretic Order Allocation

Abstract

The growing demand for small-lot, multi-variety e-commerce deliveries has intensified operational workloads in warehouse picking operations. Goods-to-Person (GTP) systems employing Autonomous Mobile Robots (AMRs) have been widely adopted to address these challenges. While substantial research has focused on task allocation and path planning for AMRs, the problem of order allocation-assigning ordered items to workstations-has received relatively little attention, despite its significant influence on system throughput. This paper proposes a novel order allocation method for dynamic GTP systems based on two-sided matching theory. Specifically, we extend the Deferred Acceptance algorithm to balance AMR transport efficiency and workstation utilization, leading to significant improvements in system throughput. Extensive simulations demonstrate that the proposed approach consistently improves throughput compared with conventional baseline methods, particularly under rapidly changing order arrivals. In addition, the computation time remains sufficiently low for real-time deployment.