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Article: A cooperative coevolutionary algorithm for design of platform-based mass customized products

TitleA cooperative coevolutionary algorithm for design of platform-based mass customized products
Authors
KeywordsCooperative coevolutionary algorithm
Mass customization
Platform product customization
Product platform
Product variant
Issue Date2008
PublisherSpringer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=0956-5515
Citation
Journal Of Intelligent Manufacturing, 2008, v. 19 n. 5, p. 507-519 How to Cite?
AbstractAs a new business model, mass customization (MC) intends to enable enterprises to comply with customer requirements at mass production efficiencies. A widely advocated approach to implement MC is platform product customization (PPC). In this approach, a product variant is derived from a given product platform to satisfy customer requirements. Adaptive PPC is such a PPC mode in which the given product platform has a modular architecture where customization is achieved by swapping standard modules and/or scaling modular components to formulate multiple product variants according to market segments and customer requirements. Adaptive PPC optimization includes structural configuration and parametric optimization. This paper presents a new method, namely, a cooperative coevolutionary algorithm (CCEA), to solve the two interrelated problems of structural configuration and parametric optimization in adaptive PPC. The performance of the proposed algorithm is compared with other methods through a set of computational experiments. The results show that CCEA outperforms the existing hierarchical evolutionary approaches, especially for large-scale problems tested in the experiments. From the experiments, it is also noticed that CCEA is slow to converge at the beginning of evolutionary process. This initial slow convergence property of the method improves its searching capability and ensures a high quality solution. © 2008 Springer Science+Business Media, LLC.
Persistent Identifierhttp://hdl.handle.net/10722/58860
ISSN
2015 Impact Factor: 1.995
2015 SCImago Journal Rankings: 1.397
ISI Accession Number ID
Funding AgencyGrant Number
NSFC70629002
University of Bath
Funding Information:

Financial supports from NSFC(# 70629002) and HKU CRCG are gratefully acknowledged for this research. Miss Li is also grateful for the financial support made available for her exchange research at University of Bath. Authors are grateful to referees and editors for their constructive suggestions for improving this paper.

References

 

DC FieldValueLanguage
dc.contributor.authorLi, Len_HK
dc.contributor.authorHuang, GQen_HK
dc.contributor.authorNewman, STen_HK
dc.date.accessioned2010-05-31T03:38:18Z-
dc.date.available2010-05-31T03:38:18Z-
dc.date.issued2008en_HK
dc.identifier.citationJournal Of Intelligent Manufacturing, 2008, v. 19 n. 5, p. 507-519en_HK
dc.identifier.issn0956-5515en_HK
dc.identifier.urihttp://hdl.handle.net/10722/58860-
dc.description.abstractAs a new business model, mass customization (MC) intends to enable enterprises to comply with customer requirements at mass production efficiencies. A widely advocated approach to implement MC is platform product customization (PPC). In this approach, a product variant is derived from a given product platform to satisfy customer requirements. Adaptive PPC is such a PPC mode in which the given product platform has a modular architecture where customization is achieved by swapping standard modules and/or scaling modular components to formulate multiple product variants according to market segments and customer requirements. Adaptive PPC optimization includes structural configuration and parametric optimization. This paper presents a new method, namely, a cooperative coevolutionary algorithm (CCEA), to solve the two interrelated problems of structural configuration and parametric optimization in adaptive PPC. The performance of the proposed algorithm is compared with other methods through a set of computational experiments. The results show that CCEA outperforms the existing hierarchical evolutionary approaches, especially for large-scale problems tested in the experiments. From the experiments, it is also noticed that CCEA is slow to converge at the beginning of evolutionary process. This initial slow convergence property of the method improves its searching capability and ensures a high quality solution. © 2008 Springer Science+Business Media, LLC.en_HK
dc.languageengen_HK
dc.publisherSpringer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=0956-5515en_HK
dc.relation.ispartofJournal of Intelligent Manufacturingen_HK
dc.subjectCooperative coevolutionary algorithmen_HK
dc.subjectMass customizationen_HK
dc.subjectPlatform product customizationen_HK
dc.subjectProduct platformen_HK
dc.subjectProduct varianten_HK
dc.titleA cooperative coevolutionary algorithm for design of platform-based mass customized productsen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0956-5515&volume=19&issue=5&spage=507&epage=&date=2008&atitle=A+cooperative+coevolutionary+algorithm+for+design+of+platform-based+mass+customized+products+en_HK
dc.identifier.emailHuang, GQ:gqhuang@hkucc.hku.hken_HK
dc.identifier.authorityHuang, GQ=rp00118en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/s10845-008-0137-xen_HK
dc.identifier.scopuseid_2-s2.0-52349089809en_HK
dc.identifier.hkuros149745en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-52349089809&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume19en_HK
dc.identifier.issue5en_HK
dc.identifier.spage507en_HK
dc.identifier.epage519en_HK
dc.identifier.isiWOS:000259438300002-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridLi, L=36985993400en_HK
dc.identifier.scopusauthoridHuang, GQ=7403425048en_HK
dc.identifier.scopusauthoridNewman, ST=7402545830en_HK

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