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Article: A genetic scheduling methodology for virtual cellular manufacturing systems: An industrial application

TitleA genetic scheduling methodology for virtual cellular manufacturing systems: An industrial application
Authors
KeywordsGenetic algorithm
Production scheduling
Virtual cellular manufacturing systems
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. 2423-2450 How to Cite?
AbstractEffective solutions to the cell formation and the production scheduling problems are vital in the design of virtual cellular manufacturing systems (VCMSs). This paper presents a new mathematical model and a scheduling algorithm based on the techniques of genetic algorithms for solving such problems. The objectives are: (1) to minimize the total materials and components travelling distance incurred in manufacturing the products, and (2) to minimize the sum of the tardiness of all products. The proposed algorithm differs from the canonical genetic algorithms in that the populations of candidate solutions consist of individuals of different age groups, and that each individual's birth and survival rates are governed by predefined aging patterns. The condition governing the birth and survival rates is developed to ensure a stable search process. In addition, Markov Chain analysis is used to investigate the convergence properties of the genetic search process theoretically. The results obtained indicate that if the individual representing the best candidate solution obtained is maintained throughout the search process, the genetic search process converges to the global optimal solution exponentially. The proposed methodology is applied to design the manufacturing system of a company in China producing component parts for internal combustion engines. The performance of the proposed age-based genetic algorithm is compared with that of the conventional genetic algorithm based on this industrial case. The results show that the methodology proposed in this paper provides a simple, effective and efficient method for solving the manufacturing cell formation and production scheduling problems for VCMSs. © 2005 Taylor & Francis Group Ltd.
Persistent Identifierhttp://hdl.handle.net/10722/74509
ISSN
2015 Impact Factor: 1.693
2015 SCImago Journal Rankings: 1.445
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorMak, KLen_HK
dc.contributor.authorLau, JSKen_HK
dc.contributor.authorWang, XXen_HK
dc.date.accessioned2010-09-06T07:02:01Z-
dc.date.available2010-09-06T07:02:01Z-
dc.date.issued2005en_HK
dc.identifier.citationInternational Journal Of Production Research, 2005, v. 43 n. 12, p. 2423-2450en_HK
dc.identifier.issn0020-7543en_HK
dc.identifier.urihttp://hdl.handle.net/10722/74509-
dc.description.abstractEffective solutions to the cell formation and the production scheduling problems are vital in the design of virtual cellular manufacturing systems (VCMSs). This paper presents a new mathematical model and a scheduling algorithm based on the techniques of genetic algorithms for solving such problems. The objectives are: (1) to minimize the total materials and components travelling distance incurred in manufacturing the products, and (2) to minimize the sum of the tardiness of all products. The proposed algorithm differs from the canonical genetic algorithms in that the populations of candidate solutions consist of individuals of different age groups, and that each individual's birth and survival rates are governed by predefined aging patterns. The condition governing the birth and survival rates is developed to ensure a stable search process. In addition, Markov Chain analysis is used to investigate the convergence properties of the genetic search process theoretically. The results obtained indicate that if the individual representing the best candidate solution obtained is maintained throughout the search process, the genetic search process converges to the global optimal solution exponentially. The proposed methodology is applied to design the manufacturing system of a company in China producing component parts for internal combustion engines. The performance of the proposed age-based genetic algorithm is compared with that of the conventional genetic algorithm based on this industrial case. The results show that the methodology proposed in this paper provides a simple, effective and efficient method for solving the manufacturing cell formation and production scheduling problems for VCMSs. © 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 algorithmen_HK
dc.subjectProduction schedulingen_HK
dc.subjectVirtual cellular manufacturing systemsen_HK
dc.titleA genetic scheduling methodology for virtual cellular manufacturing systems: An industrial applicationen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0020-7543&volume=43&issue=12&spage=2423&epage=2450&date=2005&atitle=A+genetic+scheduling+methodology+for+virtual+cellular+manufacturing+systems:+an+industrial+applicationen_HK
dc.identifier.emailMak, KL:makkl@hkucc.hku.hken_HK
dc.identifier.authorityMak, KL=rp00154en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1080/00207540500046020en_HK
dc.identifier.scopuseid_2-s2.0-27744510554en_HK
dc.identifier.hkuros107586en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-27744510554&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume43en_HK
dc.identifier.issue12en_HK
dc.identifier.spage2423en_HK
dc.identifier.epage2450en_HK
dc.identifier.isiWOS:000229181900005-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridMak, KL=7102680226en_HK
dc.identifier.scopusauthoridLau, JSK=8982533400en_HK
dc.identifier.scopusauthoridWang, XX=9246057600en_HK
dc.identifier.citeulike203910-

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