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Conference Paper: Mining standard operation times for real-time advanced production planning and scheduling from RFID-enabled shopfloor data

TitleMining standard operation times for real-time advanced production planning and scheduling from RFID-enabled shopfloor data
Authors
KeywordsAPS
Data mining
RFID
Standard Operation Time (SOT)
Issue Date2013
PublisherIFAC.
Citation
The 7th IFAC Conference on Manufacturing Modelling, Management, and Control (MIM 2013), Saint Petersburg, Russia, 19-21 June 2013. In MIM '2013 Postconference Proceedings, 2013, v. 7 pt. 1, p. 1950-1955 How to Cite?
AbstractProduction planning and scheduling require standard operation times (SOTs) which have been obtained from time studies or based on past experiences. Wide variations exist and frequently cause serious discrepancies in executing plans and schedules. Radio frequency identification (RFID) technology has recently been applied to create a real-time ubiquitous manufacturing environment, where real-time shopfloor operational data about men, machines, materials, and orders could be captured and collected. Such data carry invaluable information and knowledge which might be used for supporting advanced production planning and scheduling (APS). APS usually needs precise SOTs for perfect decision-making within the RFID-enabled real-time ubiquitous manufacturing environment. This paper proposes a data mining model to estimate realistic SOTs and their standard deviations from RFID-enabled shopfloor data. Key impact factors on SOTs are examined, including working shifts, different machines, gender, and technology complexity. It is observed that working shifts and the learning curves of three types of operators (junior, intermediate, and senior) greatly influence the SOTs. The other factors have minor affection in this case. Considering the two significant impact factors, precise and reasonable SOTs could be worked out, aiming at improving the quality and stability of production plans and schedules. © IFAC.
Persistent Identifierhttp://hdl.handle.net/10722/189944
ISBN
ISSN

 

DC FieldValueLanguage
dc.contributor.authorZhong, Ren_US
dc.contributor.authorHuang, GQen_US
dc.contributor.authorDai, QYen_US
dc.date.accessioned2013-09-17T15:03:12Z-
dc.date.available2013-09-17T15:03:12Z-
dc.date.issued2013en_US
dc.identifier.citationThe 7th IFAC Conference on Manufacturing Modelling, Management, and Control (MIM 2013), Saint Petersburg, Russia, 19-21 June 2013. In MIM '2013 Postconference Proceedings, 2013, v. 7 pt. 1, p. 1950-1955en_US
dc.identifier.isbn978-3-902823-35-9-
dc.identifier.issn1474-6670-
dc.identifier.urihttp://hdl.handle.net/10722/189944-
dc.description.abstractProduction planning and scheduling require standard operation times (SOTs) which have been obtained from time studies or based on past experiences. Wide variations exist and frequently cause serious discrepancies in executing plans and schedules. Radio frequency identification (RFID) technology has recently been applied to create a real-time ubiquitous manufacturing environment, where real-time shopfloor operational data about men, machines, materials, and orders could be captured and collected. Such data carry invaluable information and knowledge which might be used for supporting advanced production planning and scheduling (APS). APS usually needs precise SOTs for perfect decision-making within the RFID-enabled real-time ubiquitous manufacturing environment. This paper proposes a data mining model to estimate realistic SOTs and their standard deviations from RFID-enabled shopfloor data. Key impact factors on SOTs are examined, including working shifts, different machines, gender, and technology complexity. It is observed that working shifts and the learning curves of three types of operators (junior, intermediate, and senior) greatly influence the SOTs. The other factors have minor affection in this case. Considering the two significant impact factors, precise and reasonable SOTs could be worked out, aiming at improving the quality and stability of production plans and schedules. © IFAC.-
dc.languageengen_US
dc.publisherIFAC.-
dc.relation.ispartofMIM '2013 Postconference Proceedingsen_US
dc.subjectAPS-
dc.subjectData mining-
dc.subjectRFID-
dc.subjectStandard Operation Time (SOT)-
dc.titleMining standard operation times for real-time advanced production planning and scheduling from RFID-enabled shopfloor dataen_US
dc.typeConference_Paperen_US
dc.identifier.emailZhong, R: zhongzry@hku.hken_US
dc.identifier.emailHuang, GQ: gqhuang@hku.hken_US
dc.identifier.authorityHuang, GQ=rp00118en_US
dc.identifier.doi10.3182/20130619-3-RU-3018.00166-
dc.identifier.scopuseid_2-s2.0-84884336580-
dc.identifier.hkuros224417en_US
dc.identifier.volume7-
dc.identifier.issuept. 1-
dc.identifier.spage1950-
dc.identifier.epage1955-
dc.customcontrol.immutablesml 150121-

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