Dynamic cobot order picking strategies for a pick-support AGV system

In a human-robot collaborative pick environment, robots work alongside pickers to improve the pick efficiency by reducing the pickers’ unproductive walking time. However, the optimal picker deployment strategy – pooling pickers across all zones (parallel) or dedicating pickers to a warehouse zone (sequential) – is not clear. In this research, we compare two picker strategies, parallel and sequential, using dynamic models. First, we develop queuing network models to obtain load-dependent pick throughput rates corresponding to a fixed number of AGVs and picker deployment strategy. Then, we develop a Markov-decision model to investigate how higher pick performance can be achieved with fixed AGV resources and a dynamic pick strategy. Our results indicate that switching between picker allocation strategies can substantially reduce the overall system costs by 14%.