Trading off travel distance and fatigue. The effect of storage, order batching, and pod selection in robotic mobile fulfillment systems

Many e-commerce warehouses use robotic mobile fulfillment system (RMFS), where humans collaborate with robots to pick the orders. The performance of such systems depends on the joint performance of robots and humans. The performance of the workers is affected by fatigue, or the energy that it takes them to pick the items. In this paper, we study the effect of scattered storage assignment, order batching, and pod selection to minimise the total picker energy expenditure and the total robot transport distance. We introduce a mixed-integer programming formulation (called JIOPP) and introduce the NSGAII-ILS algorithm to heuristically solve it for real-world instances. Extensive numerical experiments on real-world instances show that NSGAII-ILS is competitive compared to state-of-the-art algorithms and can find Pareto solution sets that are closer to the true Pareto frontier. We evaluate the effects of batch sizes, the number of pod layers, and different pod selection policies. The results show that batching orders can save more than 35Raj of the picker's energy expenditure and more than 70Raj of the robot's transportation distance. Using the ‘golden zone’ layers on the pod and selecting the right pod for retrieval are important for striking a balance between worker fatigue and order picking efficiency.