Please use this identifier to cite or link to this item: https://doi.org/10.1007/s10845-009-0365-8
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dc.titleA manufacturing-oriented approach for multi-platforming product family design with modified genetic algorithm
dc.contributor.authorLiu, Z.
dc.contributor.authorWong, Y.S.
dc.contributor.authorLee, K.S.
dc.date.accessioned2014-04-24T03:22:38Z
dc.date.available2014-04-24T03:22:38Z
dc.date.issued2011-12
dc.identifier.citationLiu, Z., Wong, Y.S., Lee, K.S. (2011-12). A manufacturing-oriented approach for multi-platforming product family design with modified genetic algorithm. Journal of Intelligent Manufacturing 22 (6) : 891-907. ScholarBank@NUS Repository. https://doi.org/10.1007/s10845-009-0365-8
dc.identifier.issn09565515
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/50816
dc.description.abstractWith highly fragmented market and increased competition, platform-based product family design has been recognized as an effective method to construct a product line that satisfies diverse customer's demands while aiming to keep design and production cost-effective. The success of the resulting product family often relies on properly resolving the inherent tradeoff between commonality across the family and performance loss. In this paper, a systematic multi-platforming product family approach is proposed to design a scale-based product family. In the light of the basic premise that increased commonality implies enhanced manufacturing efficiency, we present an effective platform decision strategy to quantify family design configuration using a commonality index that couples design varieties with production variation. Meanwhile, unlikemany existingmethods that assume a single given platform configuration, the proposed method addresses the multi-platforming configuration across the family, and can generate alternative product family solutions with different levels of commonality. A modified genetic algorithm is developed to solve the aggregated multiobjective optimization problem and an industrial example of a planetary gear train for drills is given to demonstrate the proposed method. © Springer Science+Business Media, LLC 2009.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1007/s10845-009-0365-8
dc.sourceScopus
dc.subjectCommonality index
dc.subjectDesign for manufacturing
dc.subjectGenetic algorithm
dc.subjectMulti-platforming
dc.subjectProduct family
dc.typeArticle
dc.contributor.departmentCIVIL & ENVIRONMENTAL ENGINEERING
dc.contributor.departmentMECHANICAL ENGINEERING
dc.description.doi10.1007/s10845-009-0365-8
dc.description.sourcetitleJournal of Intelligent Manufacturing
dc.description.volume22
dc.description.issue6
dc.description.page891-907
dc.description.codenJIMNE
dc.identifier.isiut000296735800007
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