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dc.contributor.authorAcebes, Fernando
dc.contributor.authorPereda, María
dc.contributor.authorPoza, David J.
dc.contributor.authorPajares Gutiérrez, Javier
dc.contributor.authorGalán Ordax, José Manuel 
dc.date.accessioned2016-02-12T09:10:38Z
dc.date.available2018-10-01T02:45:06Z
dc.date.issued2015-10
dc.identifier.issn0263-7863
dc.identifier.urihttp://hdl.handle.net/10259/3927
dc.description.abstractThe aim of this paper is to describe a new integrated methodology for project control under uncertainty. This proposal is based on Earned Value Methodology and risk analysis and presents several refinements to previous methodologies. More specifically, the approach uses extensive Monte Carlo simulation to obtain information about the expected behavior of the project. This dataset is exploited in several ways using different statistical learning methodologies in a structured fashion. Initially, simulations are used to detect if project deviations are a consequence of the expected variability using Anomaly Detection algorithms. If the project follows this expected variability, probabilities of success in cost and time and expected cost and total duration of the project can be estimated using classification and regression approachesen
dc.description.sponsorshipProject "Computational Models for Strategic Project Portfolio Management", supported by the Regional Government of Castile and Leon (Spain) with grant VA056A12-2 and by the Spanish Ministerio de Ciencia e Innovacion project CSD2010-00034 (SimulPast CONSOLIDER-INGENIO 2010).en
dc.format.mimetypeapplication/pdf
dc.language.isoenges
dc.publisherElsevieren
dc.relation.ispartofInternational journal of project management. 2015, V. 33, n. 7, p. 1597–1609en
dc.subjectProject managementen
dc.subjectEarned value managementen
dc.subjectProject controlen
dc.subjectMonte Carlo simulationen
dc.subjectProject risk managementen
dc.subjectStatistical learningen
dc.subjectAnomaly Detectionen
dc.subject.otherIndustrial managementen
dc.subject.otherGestión de empresases
dc.titleStochastic earned value analysis using Monte Carlo simulation and statistical learning techniquesen
dc.typeinfo:eu-repo/semantics/article
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.relation.publisherversionhttp://dx.doi.org/10.1016/j.ijproman.2015.06.012
dc.relation.projectIDinfo:eu-repo/grantAgreement/MICINN/CSD2010-00034
dc.relation.projectIDinfo:eu-repo/grantAgreement/JCyL/VA056A12-2
dc.type.hasVersioninfo:eu-repo/semantics/submittedVersionen


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