Analytical models for an automated storage and retrieval system with multiple in-the-aisle pick positions

Abstract

An automated storage and retrieval system with multiple in-the-aisle pick positions (MIAPP-AS/RS) is a case-level order fulfillment technology that enables order picking via multiple pick positions (outputs) located in the aisle. This article develops expected travel time models for different operating policies and different physical configurations. These models can be used to analyze MIAPP-AS/RS throughput performance during peak and non-peak hours. Moreover, closed-form approximations are derived for the case of an infinite number of pick positions, which enable the optimal shape configuration that minimizes expected travel times to be derived. The expected travel time models are compared with a simulation model of a discrete rack, and the results validate that the proposed models provide good estimates. Finally, a numerical experiment is conducted to illustrate the trade-offs between performance of operating policies and design configurations. It is found that MIAPP-AS/RS with a dual picking floor and input point is a robust configuration due to the single command operating policy having a comparable throughput performance to a dual-command operating policy.

Keywords:

• AS/RS
• travel-time models
• multiple I/O points

Additional information

Notes on contributors

Faraz Ramtin

Faraz Ramtin received a B.S. degree in Industrial Engineering from Shahid Beheshti University, Tehran, Iran. Also, he received an M.S. degree in Industrial Engineering from Amirkabir University of Technology, Tehran, Iran. He is currently working toward his Ph.D. degree in Industrial Engineering at the University of Central Florida. His research interests include applying Operations Research techniques to facility logistics and material handling problems.

Jennifer A. Pazour

Jennifer A. Pazour is an Assistant Professor of Industrial Engineering and Management Systems at the University of Central Florida. She holds three degrees in Industrial Engineering (a B.S. from South Dakota School of Mines and Technology, as well as an M.S. and Ph.D. from the University of Arkansas).  Her research interests involve developing mathematical models to aid in the understanding and dynamics of logistics challenges and application areas include distribution center design, material handling systems, health care, military, and transportation. She is a recipient of the Young Investigator Program from the Office of Naval Research (2013), a Research Start Up grant from the Material Handling Institute (2013), and a Doctoral Dissertation Enhancement Project from the National Science Foundation (2010).