Velocity-Based Storage Assignment in Semi-Automated Storage Systems

Abstract

Our research focuses on the storage decision in a semi-automated storage system. In a semi-automated storage system, the inventory is stored on mobile storage pods. In a typical system, each storage pod carries a mixture of items, and the inventory of each item is spread over multiple storage pods. The storage pods are mobile in that a pod can be lifted and transported by a robotic drive. These storage pods are stored within a storage zone that has stationary stations for picking and stowing on its boundary. The robotic drives transport the pods to these stations at which operators conduct pick or stow operations. The storage decision is to decide to which storage location within the storage zone to return a pod upon the completion of a pick or stow operation. The storage decision has a direct impact on the total travel time, and hence the workload of the robotic drives. We develop a fluid model to analyze the performance of velocity-based storage policies. With this model, we can characterize the possible improvement from applying a velocity-based storage policy in comparison to the random storage policy that returns the pod to a randomly-chosen storage location. Within the category of velocity-based storage, we show that class-based storage with two or three classes can achieve most of the benefits from full-velocity storage. We show that the benefits from velocity-based storage increase with greater variation in the pod velocities. To validate the fluid model we build a discrete-time simulator with real industry data. We observe an 8% to 11% reduction in the travel distance with 2-class or 3-class storage system, depending on the parameter settings. From a sensitivity analysis we establish the robustness of the class-based storage policies as they continue to perform well under a broad range of warehouse settings including different zoning strategies, resource utilization levels and space utilization levels.