Analysis and Design of Rack-climbing Robotic Storage and Retrieval Systems

Wanying Chen, Rene de Koster, Yeming Gong

Research output: Contribution to journalArticleAcademicpeer-review

17 Citations (Scopus)

Abstract

Warehouses are becoming increasingly robotized. Autonomous rack-climbing robots have recently been introduced in e-commerce fulfillment centers. The robots not only retrieve loads from any level in a rack but also, roam the warehouse and bring the loads to order picking stations without using conveyors or lifts. This paper models and analyzes this system under both single and dual commands with different robot assignment (dedicated versus shared) and storage location assignment (class-based and random) policies. We study these policies in the presence of robot congestion. We evaluate the impact of two blocking protocols, a wait-outside-aisle policy and a block-and-recirculate policy, on the order throughput time. The system is modeled using semiopen queuing networks (SOQNs) for the different operating policies. The analytical models are validated using simulation. We also use this model to compare this system with a shuttle-based system. The results show that (1) the choice of the wait-outside-aisle policy or the block-and-recirculate policy mainly depends on the number of the robots in the system and the throughput requirement and that (2) the dedicated robot assignment policy can be an attractive policy, especially for a large system.
Original languageEnglish
Pages (from-to)1409-1722
Number of pages19
JournalTransportation Science
Volume56
Issue number6
DOIs
Publication statusPublished - Nov 2022

Bibliographical note

Funding Information:
Funding: W. Chen was supported by the National Natural Science Foundation of China [Grant 72001189] and Qianjiang River Talent Scholarship [Grant QJC1802002]. Y. Gong is supported by the Artificial Intelligence in Management Institute and Business Intelligence Center. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.1140.

Publisher Copyright:
© 2022 INFORMS.

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