RT Artículo
T1 Stochastic earned value analysis using Monte Carlo simulation and statistical learning techniques
A1 Acebes, Fernando
A1 Pereda, María
A1 Poza, David J.
A1 Pajares Gutiérrez, Javier
A1 Galán Ordax, José Manuel
K1 Project management
K1 Earned value management
K1 Project control
K1 Monte Carlo simulation
K1 Project risk management
K1 Statistical learning
K1 Anomaly Detection
AB 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
PB Elsevier
SN 0263-7863
YR 2015
FD 2015-10
LK http://hdl.handle.net/10259/3927
UL http://hdl.handle.net/10259/3927
LA eng
NO 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).
DS Repositorio Institucional de la Universidad de Burgos
RD 28-oct-2021