Ergonomic Task Allocation in an AMR-Assisted Order Picking System

Order picking, a widely studied process in warehousing, is experiencing a growing implementation of automation. Among all the market-ready technologies, autonomous mobile robots (AMRs) have been receiving attention from both academics and practitioners lately. By utilizing AMRs, human pickers are relieved of the task of transporting items, typically performed using picking carts, giving rise to the term Assisted Order Picking Systems (AOPSs). However, the resulting changes in human work processes and the associated potential injury risks for humans working in these systems have largely been unaddressed so far. Caution is required due to the increased frequency of picking actions in AOPSs, which have been shown to be closely associated with potential musculoskeletal disorders. To address this research gap, we model the human work process in AOPSs, emphasizing task allocation within a homogenous human team and routing based on the multiple traveling salesman problem. Additionally, we consider human fatigue and develop a task allocation method that synchronizes human duty cycles within the wave picking strategy. Initial results from illustrative experiments demonstrate the impact of fatigue on the order picking makespan, albeit with the limitation of a simplified human-robot collaboration mechanism. Further issues related to model extensions and heuristic design are discussed in this paper and will be explored in future studies.