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Article: A genetic algorithm-based approach to machine assignment problem

TitleA genetic algorithm-based approach to machine assignment problem
Authors
KeywordsGenetic algorithms
Job-shop scheduling
Machine assignment
Machining flexibility
Issue Date2005
PublisherTaylor & Francis Ltd. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/00207543.asp
Citation
International Journal Of Production Research, 2005, v. 43 n. 12, p. 2451-2472 How to Cite?
AbstractOver the last few decades, production scheduling problems have received much attention. Due to global competition, it is important to have a vigorous control on production costs while keeping a reasonable level of production capability and customer satisfaction. One of the most important factors that continuously impacts on production performance is machining flexibility, which can reduce the overall production lead-time, work-in-progress inventories, overall job lateness, etc. It is also vital to balance various quantitative aspects of this flexibility which is commonly regarded as a major strategic objective of many firms. However, this aspect has not been studied in a practical way related to the present manufacturing environment. In this paper, an assignment and scheduling model is developed to study the impact of machining flexibility on production issues such as job lateness and machine utilisation. A genetic algorithm-based approach is developed to solve a generic machine assignment problem using standard benchmark problems and real industrial problems in China. Computational results suggest that machining flexibility can improve the overall production performance if the equilibrium state can be quantified between scheduling performance and capital investment. Then production planners can determine the investment plan in order to achieve a desired level of scheduling performance. © 2005 Taylor & Francis Group Ltd.
Persistent Identifierhttp://hdl.handle.net/10722/74479
ISSN
2015 Impact Factor: 1.693
2015 SCImago Journal Rankings: 1.445
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorChan, FTSen_HK
dc.contributor.authorWong, TCen_HK
dc.contributor.authorChan, LYen_HK
dc.date.accessioned2010-09-06T07:01:44Z-
dc.date.available2010-09-06T07:01:44Z-
dc.date.issued2005en_HK
dc.identifier.citationInternational Journal Of Production Research, 2005, v. 43 n. 12, p. 2451-2472en_HK
dc.identifier.issn0020-7543en_HK
dc.identifier.urihttp://hdl.handle.net/10722/74479-
dc.description.abstractOver the last few decades, production scheduling problems have received much attention. Due to global competition, it is important to have a vigorous control on production costs while keeping a reasonable level of production capability and customer satisfaction. One of the most important factors that continuously impacts on production performance is machining flexibility, which can reduce the overall production lead-time, work-in-progress inventories, overall job lateness, etc. It is also vital to balance various quantitative aspects of this flexibility which is commonly regarded as a major strategic objective of many firms. However, this aspect has not been studied in a practical way related to the present manufacturing environment. In this paper, an assignment and scheduling model is developed to study the impact of machining flexibility on production issues such as job lateness and machine utilisation. A genetic algorithm-based approach is developed to solve a generic machine assignment problem using standard benchmark problems and real industrial problems in China. Computational results suggest that machining flexibility can improve the overall production performance if the equilibrium state can be quantified between scheduling performance and capital investment. Then production planners can determine the investment plan in order to achieve a desired level of scheduling performance. © 2005 Taylor & Francis Group Ltd.en_HK
dc.languageengen_HK
dc.publisherTaylor & Francis Ltd. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/00207543.aspen_HK
dc.relation.ispartofInternational Journal of Production Researchen_HK
dc.subjectGenetic algorithmsen_HK
dc.subjectJob-shop schedulingen_HK
dc.subjectMachine assignmenten_HK
dc.subjectMachining flexibilityen_HK
dc.titleA genetic algorithm-based approach to machine assignment problemen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0020-7543&volume=43&issue=12&spage=2451&epage=2472&date=2005&atitle=A+genetic+algorithm-based+approach+to+machine+assignment+problemen_HK
dc.identifier.emailChan, FTS: ftschan@hkucc.hku.hken_HK
dc.identifier.emailChan, LY: plychan@hku.hken_HK
dc.identifier.authorityChan, FTS=rp00090en_HK
dc.identifier.authorityChan, LY=rp00093en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1080/00207540500045956en_HK
dc.identifier.scopuseid_2-s2.0-27744463120en_HK
dc.identifier.hkuros100310en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-27744463120&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume43en_HK
dc.identifier.issue12en_HK
dc.identifier.spage2451en_HK
dc.identifier.epage2472en_HK
dc.identifier.isiWOS:000229181900006-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridChan, FTS=7202586517en_HK
dc.identifier.scopusauthoridWong, TC=9246060100en_HK
dc.identifier.scopusauthoridChan, LY=7403540482en_HK
dc.identifier.citeulike203909-

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