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dc.contributor.authorWauters, Mathieu*
dc.contributor.authorVanhoucke, Mario*
dc.contributor.authorWauters, Mathieu
dc.contributor.authorVanhoucke, Mario
dc.date.accessioned2017-12-02T14:52:52Z
dc.date.available2017-12-02T14:52:52Z
dc.date.issued2014
dc.identifier.doi10.1016/j.autcon.2014.07.014
dc.identifier.urihttp://hdl.handle.net/20.500.12127/5020
dc.description.abstractSupport Vector Machines are methods that stem from Artificial Intelligence and attempt to learn the relation between data inputs and one or multiple output values. However, the application of these methods has barely been explored in a project control context. In this paper, a forecasting analysis is presented that compares the proposed Support Vector Regression model with the best performing Earned Value and Earned Schedule methods. The parameters of the SVM are tuned using a cross-validation and grid search procedure, after which a large computational experiment is conducted. The results show that the Support Vector Machine Regression outperforms the currently available forecasting methods. Additionally, a robustness experiment has been set up to investigate the performance of the proposed method when the discrepancy between training and test set becomes larger.
dc.language.isoen
dc.subjectOperations & Supply Chain Management
dc.subjectEarned Value Management (EVM)
dc.subjectSupport Vector Regression (SVR)
dc.subjectPrediction
dc.titleSupport vector machine regression for project control forecasting
dc.identifier.journalAutomation in Construction
dc.source.volume47
dc.source.issueNovember
dc.source.beginpage92
dc.source.endpage106
vlerick.knowledgedomainOperations & Supply Chain Management
vlerick.typearticleJournal article with impact factor
vlerick.vlerickdepartmentTOM
dc.identifier.vperid58614
dc.identifier.vperid176997
dc.identifier.vpubid6226


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