Ergonomic and economic evaluation of a collaborative hybrid order picking system

Abstract

Warehouses are important nodes in almost every supply chain. Within warehouses, order picking is a crucial task that is extremely time- and cost-intensive. While order picking systems (OPSs) have traditionally been operated manually, new technologies offer opportunities for reducing the workload of warehouse workers. These technologies include autonomous picking robots that can function in combination with human pickers within a shared workspace. This technology enables human–robot collaboration and enhances flexibility in system design, as robots can either support humans or work independently. Research on the advantages of these hybrid OPSs (HOPSs) for improving operational performance is still scarce, however. To contribute to closing this research gap, we propose an agent-based simulation model to investigate how HOPSs reduce the daily workload of human order pickers. The results reveal that HOPSs – if certain assignment rules for the picking tasks are considered – can reduce both the operational costs of the system and human workload compared to a pure manual or a fully automated OPS. Nonetheless, attention should be paid to control the item weight pickers are supposed to handle, as HOPSs reduce the travel distance of human pickers, resulting in a higher frequency of picking activities and an increased ergonomic risk for musculoskeletal disorders.