DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Jun-Ho | ko |
dc.contributor.author | Kim, Hyun-Jung | ko |
dc.date.accessioned | 2022-05-06T08:03:44Z | - |
dc.date.available | 2022-05-06T08:03:44Z | - |
dc.date.created | 2021-03-17 | - |
dc.date.created | 2021-03-17 | - |
dc.date.issued | 2022-04 | - |
dc.identifier.citation | INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, v.60, no.7, pp.2346 - 2368 | - |
dc.identifier.issn | 0020-7543 | - |
dc.identifier.uri | http://hdl.handle.net/10203/296420 | - |
dc.description.abstract | We address a robotic flow shop scheduling problem where two part types are processed on each given set of dedicated machines. A single robot moving on a fixed rail transports one part at a time, and the processing times of the parts vary on the machines within a given time interval. We use a reinforcement learning (RL) approach to obtain efficient robot task sequences to minimise makespan. We model the problem with a Petri net used for a RLenvironment and develop a lower bound for the makespan. We then define states, actions, and rewards based on the Petri net model; further, we show that the RL approach works better than the first-in-first-out (FIFO) rule and the reverse sequence (RS), which is extensively used for cyclic scheduling of a robotic flow shop; moreover, the gap between the makespan from the proposed algorithm and a lower bound is not large; finally, the makespan from the RL method is compared to an optimal solution in a relaxed problem. This research shows the applicability of RL for the scheduling of robotic flow shops and its efficiency by comparing it to FIFO, RS and a lower bound. This work can be easily extended to several other variants of robotic flow shop scheduling problems. | - |
dc.language | English | - |
dc.publisher | TAYLOR & FRANCIS LTD | - |
dc.title | Reinforcement learning for robotic flow shop scheduling with processing time variations | - |
dc.type | Article | - |
dc.identifier.wosid | 000620050100001 | - |
dc.identifier.scopusid | 2-s2.0-85101246821 | - |
dc.type.rims | ART | - |
dc.citation.volume | 60 | - |
dc.citation.issue | 7 | - |
dc.citation.beginningpage | 2346 | - |
dc.citation.endingpage | 2368 | - |
dc.citation.publicationname | INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH | - |
dc.identifier.doi | 10.1080/00207543.2021.1887533 | - |
dc.contributor.localauthor | Kim, Hyun-Jung | - |
dc.contributor.nonIdAuthor | Lee, Jun-Ho | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Processing time variation | - |
dc.subject.keywordAuthor | Petri net | - |
dc.subject.keywordAuthor | scheduling | - |
dc.subject.keywordAuthor | reinforcement learning | - |
dc.subject.keywordAuthor | robotic flow shop | - |
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