Velocity-based Storage Assignment in Semi-automated Storage Systems
Name
Velocity-based Storage Assignment in Semi-automated Storage Systems June 2018.pdf
Description
Main article to be published
Size
11.62 MB
Format
Adobe PDF
Checksum (MD5)
318f65145c518ed3b4d672f9bbbe9ea8
Author(s)
Cezik, Tolga
Yuan, Rong
Graves, Stephen C
Date Issued
June 2018
Journal
Production and Operations Management
Citation
Yuan, Rong, Tolga Cezik, and Stephen C. Graves. “Velocity-Based Storage Assignment in Semi-Automated Storage Systems.” Production and Operations Management (June 20, 2018).
Version
Author's final manuscript
Abstract
Our research focuses on the storage decision in a semi-automated storage system, where 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. These pods are transported by robotic drives to stationary stations on the boundary of the storage zone where associates 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. We characterize the maximum possible improvement from applying a velocity-based storage policy in comparison to the random storage policy. We show that class-based storage with two or three classes can achieve most of the potential benefits and that these benefits increase with greater variation in the pod velocities. To validate the findings, we build a discrete-time simulator with real industry data. We observe an 8% to 10% reduction in the travel distance with a 2-class or 3-class storage policy, 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 and space utilization levels.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Massachusetts Institute of Technology. Institute for Data, Systems, and Society
Massachusetts Institute of Technology. Computation for Design and Optimization Program
Sloan School of Management
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1111/poms.12925
