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dc.contributor.authorYu, Guodong
dc.contributor.authorYu, Yang
dc.contributor.authorXing, Qingsong
dc.contributor.authorLi, Fei
dc.date.accessioned2014-04-10T11:20:36Z
dc.date.available2014-04-10T11:20:36Z
dc.date.issued2014-04
dc.identifier.citationYu, Guodong [et al.]. Research on the time optimization model algorithm of Customer Collaborative Product Innovation. "Journal of Industrial Engineering and Management", Abril 2014, vol. 7, núm. 1, p. 137-152.
dc.identifier.issn2013-0953
dc.identifier.urihttp://hdl.handle.net/2099/14497
dc.description.abstractPurpose: To improve the efficiency of information sharing among the innovation agents of customer collaborative product innovation and shorten the product design cycle, an improved genetic annealing algorithm of the time optimization was presented. Design/methodology/approach: Based on the analysis of the objective relationship between the design tasks, the paper takes job shop problems for machining model and proposes the improved genetic algorithm to solve the problems, which is based on the niche technology and thus a better product collaborative innovation design time schedule is got to improve the efficiency. Finally, through the collaborative innovation design of a certain type of mobile phone, the proposed model and method were verified to be correct and effective. Findings and Originality/value: An algorithm with obvious advantages in terms of searching capability and optimization efficiency of customer collaborative product innovation was proposed. According to the defects of the traditional genetic annealing algorithm, the niche genetic annealing algorithm was presented. Firstly, it avoided the effective gene deletions at the early search stage and guaranteed the diversity of solution; Secondly, adaptive double point crossover and swap mutation strategy were introduced to overcome the defects of long solving process and easily converging local minimum value due to the fixed crossover and mutation probability; Thirdly, elite reserved strategy was imported that optimal solution missing was avoided effectively and evolution speed was accelerated. Originality/value: Firstly, the improved genetic simulated annealing algorithm overcomes some defects such as effective gene easily lost in early search. It is helpful to shorten the calculation process and improve the accuracy of the convergence value. Moreover, it speeds up the evolution and ensures the reliability of the optimal solution. Meanwhile, it has obvious advantages in efficiency of information sharing among the innovation agents of customer collaborative product innovation. So, the product design cycle could be shortened.
dc.format.extent16 p.
dc.language.isoeng
dc.publisherOmniaScience
dc.rightsAttribution-NonCommercial 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Economia i organització d'empreses::Competitivitat i innovació
dc.subjectÀrees temàtiques de la UPC::Matemàtiques i estadística::Investigació operativa::Optimització
dc.subject.lcshComputer algorithms
dc.subject.lcshMathematical optimization
dc.subject.lcshProduct design -- Computer simulation
dc.subject.otherGenetic simulated annealing algorithm
dc.subject.otherNiche
dc.subject.otherCustomer Collaborative Product innovation scheduling
dc.subject.otherSimulation
dc.titleResearch on the time optimization model algorithm of Customer Collaborative Product Innovation
dc.typeArticle
dc.subject.lemacAlgorismes genètics
dc.subject.lemacOptimització matemàtica
dc.subject.lemacDisseny de producte -- Simulació per ordinador
dc.identifier.dlB-28744-2008
dc.description.peerreviewedPeer Reviewed
dc.rights.accessOpen Access
local.citation.authorYu, Guodong; Yu, Yang; Xing, Qingsong; Li, Fei
local.citation.publicationNameJournal of Industrial Engineering and Management
local.citation.volume7
local.citation.number1
local.citation.startingPage137
local.citation.endingPage152


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