Sequence learning-based schedule prediction for flexible manufacturing systems under uncertainties
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Meilanitasari, Prita | - |
dc.contributor.author | Shin, Seung-Jun | - |
dc.date.accessioned | 2022-07-06T11:35:58Z | - |
dc.date.available | 2022-07-06T11:35:58Z | - |
dc.date.created | 2022-01-05 | - |
dc.date.issued | 2021-11 | - |
dc.identifier.issn | 1742-6588 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/140422 | - |
dc.description.abstract | This study presents a method of production schedule prediction for flexible manufacturing systems with consideration of the uncertainty factors including limited machine capacity, diverse processing time and unplanned waiting time. The proposed method can predict product-level schedules using sequence learning, which derives data-learned models to predict production sequence proactively and granularly at the product-level. A decision tree technique is applied to derive such predictive models to pre-trace the locations of individual products allocated to each workstation. A deterministic technique is also applied to estimate waiting and production time per product as well as total production time consumed to fabricate all products assigned by work orders. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | IOP Publishing Ltd | - |
dc.title | Sequence learning-based schedule prediction for flexible manufacturing systems under uncertainties | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Shin, Seung-Jun | - |
dc.identifier.doi | 10.1088/1742-6596/1996/1/012010 | - |
dc.identifier.scopusid | 2-s2.0-85120475029 | - |
dc.identifier.bibliographicCitation | Journal of Physics: Conference Series, v.1996, no.1, pp.1 - 10 | - |
dc.relation.isPartOf | Journal of Physics: Conference Series | - |
dc.citation.title | Journal of Physics: Conference Series | - |
dc.citation.volume | 1996 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 10 | - |
dc.type.rims | ART | - |
dc.type.docType | Conference Paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | Decision trees | - |
dc.subject.keywordPlus | Forecasting | - |
dc.subject.keywordPlus | Learning systems | - |
dc.subject.keywordPlus | Production control | - |
dc.subject.keywordPlus | Decision tree techniques | - |
dc.subject.keywordPlus | Processing time | - |
dc.subject.keywordPlus | Production schedule | - |
dc.subject.keywordPlus | Production sequences | - |
dc.subject.keywordPlus | Production time | - |
dc.subject.keywordPlus | Schedule predictions | - |
dc.subject.keywordPlus | Sequence learning | - |
dc.subject.keywordPlus | Uncertainty | - |
dc.subject.keywordPlus | Uncertainty factors | - |
dc.subject.keywordPlus | Waiting time | - |
dc.subject.keywordPlus | Flexible manufacturing systems | - |
dc.identifier.url | https://iopscience.iop.org/article/10.1088/1742-6596/1996/1/012010 | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
222, Wangsimni-ro, Seongdong-gu, Seoul, 04763, Korea+82-2-2220-1365
COPYRIGHT © 2021 HANYANG UNIVERSITY.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.