Please use this identifier to cite or link to this item: https://doi.org/10.1080/00207543.2011.561883
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dc.titleGA-BHTR: An improved genetic algorithm for partner selection in virtual manufacturing
dc.contributor.authorTao, F.
dc.contributor.authorQiao, K.
dc.contributor.authorZhang, L.
dc.contributor.authorLi, Z.
dc.contributor.authorNee, A.Y.C.
dc.date.accessioned2014-06-17T06:22:40Z
dc.date.available2014-06-17T06:22:40Z
dc.date.issued2012-04-15
dc.identifier.citationTao, F., Qiao, K., Zhang, L., Li, Z., Nee, A.Y.C. (2012-04-15). GA-BHTR: An improved genetic algorithm for partner selection in virtual manufacturing. International Journal of Production Research 50 (8) : 2079-2100. ScholarBank@NUS Repository. https://doi.org/10.1080/00207543.2011.561883
dc.identifier.issn00207543
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/60392
dc.description.abstractAn evolutionary genetic algorithm maintained using the binary heap and transitive reduction (GA-BHTR) method for addressing the partner selection problem (PSP) in a virtual enterprise is proposed. In order to reduce the time complexity of PSP, an algorithm for simplifying the directed acyclic graph that represents the precedence relationship among the subprojects in PSP is first designed. Different from the traditional regular GA, in order to avoid solutions from converging to a constant value early during evolution, multiple communities are used instead of a single community in GA-BHTR. The method and algorithms to distribute the individuals to the multiple communities while maximising the differences among the different communities are proposed. The concept of the catastrophe is introduced in the proposed GA-BHTR in order to avoid the solutions from converging to a local best solution too early after several generations of evolution. In order to maintain the capacity of the community (i.e. the number of individuals existing in a community) at a constant value while enhancing the diversity of the proposed GA-BHTR, an algorithm using the binary heap to maintain the data is designed. Simulation and experiments are conducted to test the effectiveness and performance of the proposed GA-BHTR for addressing PSP. © 2012 Copyright Taylor and Francis Group, LLC.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1080/00207543.2011.561883
dc.sourceScopus
dc.subjectbinary heap
dc.subjectcatastrophe
dc.subjectgenetic algorithm (GA)
dc.subjectmutiple communities
dc.subjectpartner selection problem (PSP)
dc.subjecttransitive reduction
dc.subjectvirtual manufacturing
dc.typeArticle
dc.contributor.departmentMECHANICAL ENGINEERING
dc.description.doi10.1080/00207543.2011.561883
dc.description.sourcetitleInternational Journal of Production Research
dc.description.volume50
dc.description.issue8
dc.description.page2079-2100
dc.description.codenIJPRB
dc.identifier.isiut000304343200001
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