Velocity-Based Stowage Policy for Semi-Automated Fulfillment System

Online retail fulfillment is increasingly performed by semi-automated fulfillment systems in which inventory is stored in mobile pods that are moved by robotic drives. In this paper, we develop a model that explores the benefits of velocity-based stowage policies for semi-automated fulfillment systems, also known as robotic mobile fulfillment systems. The stowage policies decide which pods to replenish with the received inventory. Specifically, we model policies that account for the velocity of the units being stowed. By stowing higher (lower) velocity units on higher (lower) velocity pods, we expect to increase the heterogeneity of the pod velocities. Greater heterogeneity in pod velocities can yield a greater reduction in pod travel distance from velocity-based storage policies for the pod. Reducing pod travel distance decreases the number of robotic drives that are needed for the system to maintain a certain throughput rate (Yuan, 2016). We analyze an M-class velocity-based stowage policy. We stow units from each velocity class onto pods dedicated to that velocity class; each class of pods then has its own storage zone, where the zones are ordered based on distance to the stationary pick and stow stations. We characterize the pod travel distance as it depends on the skewness of the demand distribution across the sku assortment, and on the number and size of the classes. We find that most of the benefit from a velocity-based stowage policy can be achieved with two or three classes. For representative demand distributions, we find that a two-class stowage policy achieves 75% of the maximum possible travel-time reduction, and that a three-class policy improves this to 90%.