Synchronizing Human-Robot Cooperation in Warehouses: Integrated Order Batching and Routing Problem in AMR-Assisted Picking Systems

With technological advancements and the growth of e-commerce industries, warehouses are taking advantage of robots' assistance to gain more productivity. This increasing usage, however, has directed them to face more challenges in terms of human-robot collaboration, specifically in the order-picking process. In this study, we address the problem of human-robot coordination in a picker-to-part picking system, where automated mobile robots (AMRs) that are equipped with bins move through the warehouse to collect the items on the batch lists. Human pickers, on the other hand, travel between item locations to load the AMRs. As there are fewer human pickers than AMRs, efficient routing of both human and robot pickers plays a pivotal role in determining the productivity of the system. Hence, the main decisions of the problem are the batching of orders for the AMRs and the routes of all pickers. To address this problem, we propose a mathematical formulation and a novel two-stage decomposition-based heuristic approach to minimise the makespan. The first stage of our heuristic solves the integrated batching-routing problem for the AMRs using a variable neighbourhood search approach, and then, in the second stage, by setting AMRs' arrival times at each location as due dates for the human pickers, the algorithm searches for their routes. We also test the performance of the heuristic and generate managerial insights using randomly generated instances on different warehouse structures.