A reinforcement learning approach for transaction scheduling in a shuttle-based storage and retrieval system

dc.contributor.authorEkren, Banu Y.
dc.contributor.authorArslan, Bartu
dc.date.accessioned2022-04-14T18:06:06Z
dc.date.available2022-04-14T18:06:06Z
dc.date.issued2022-03-29
dc.description.abstractWith recent Industry 4.0 developments, companies tend to automate their industries. Warehousing companies also take part in this trend. A shuttle-based storage and retrieval system (SBS/RS) is an automated storage and retrieval system technology experiencing recent drastic market growth. This technology is mostly utilized in large distribution centers processing mini-loads. With the recent increase in e-commerce practices, fast delivery requirements with low volume orders have increased. SBS/RS provides ultrahigh-speed load handling due to having an excess amount of shuttles in the system. However, not only the physical design of an automated warehousing technology but also the design of operational system policies would help with fast handling targets. In this work, in an effort to increase the performance of an SBS/RS, we apply a machine learning (ML) (i.e., Q-learning) approach on a newly proposed tier-to-tier SBS/RS design, redesigned from a traditional tier-captive SBS/RS. The novelty of this paper is twofold: First, we propose a novel SBS/RS design where shuttles can travel between tiers in the system; second, due to the complexity of operation of shuttles in that newly proposed design, we implement an ML-based algorithm for transaction selection in that system. The ML-based solution is compared with traditional scheduling approaches: first-in-first-out and shortest process time (i.e., travel) scheduling rules. The results indicate that in most cases, the Q-learning approach performs better than the two static scheduling approaches.en_UK
dc.identifier.citationEkren BY, Arslan B. (2024) A reinforcement learning approach for transaction scheduling in a shuttle-based storage and retrieval system. International Transactions in Operational Research, Volume 31, Issue 1, January 2024, pp. 274-295en_UK
dc.identifier.issn0969-6016
dc.identifier.urihttps://doi.org/10.1111/itor.13135
dc.identifier.urihttp://dspace.lib.cranfield.ac.uk/handle/1826/17775
dc.language.isoenen_UK
dc.publisherWileyen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectSBS/RSen_UK
dc.subjectReinforcement Learningen_UK
dc.subjectQ-learningen_UK
dc.subjectSimulationen_UK
dc.subjectWarehousingen_UK
dc.subjectAutomated storageen_UK
dc.titleA reinforcement learning approach for transaction scheduling in a shuttle-based storage and retrieval systemen_UK
dc.typeArticleen_UK

Files

Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
transaction_scheduling_in_a_shuttle-based_storage-2022.pdf
Size:
1.31 MB
Format:
Adobe Portable Document Format
Description:
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.63 KB
Format:
Item-specific license agreed upon to submission
Description: