2026-04-28T16:38:49Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/39272024-05-13T10:38:17Zcom_10259_3830com_10259_5086com_10259_2604col_10259_3832

Acebes, Fernando Pereda, María Poza, David J. Pajares Gutiérrez, Javier Galán Ordax, José Manuel 2016-02-12T09:10:38Z 2018-10-01T02:45:06Z 2015-10 0263-7863 http://hdl.handle.net/10259/3927 The 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 approaches Project "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). application/pdf eng Elsevier International journal of project management. 2015, V. 33, n. 7, p. 1597–1609 http://dx.doi.org/10.1016/j.ijproman.2015.06.012 info:eu-repo/grantAgreement/MICINN/CSD2010-00034 info:eu-repo/grantAgreement/JCyL/VA056A12-2 Project management Earned value management Project control Monte Carlo simulation Project risk management Statistical learning Anomaly Detection Industrial management Gestión de empresas Stochastic earned value analysis using Monte Carlo simulation and statistical learning techniques info:eu-repo/semantics/article info:eu-repo/semantics/submittedVersion info:eu-repo/semantics/openAccess