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Cited by 120Construction Project Management and PerformanceCapital Investment and Risk AnalysisResource-Constrained Project Scheduling

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Stochastic earned value analysis using Monte Carlo simulation and statistical learning techniques

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Acebes, Fernando, Pereda, María, Poza, David, Pajares, Javier, Galán, José Manuel·2015·International Journal of Project Management
DOI 10.1016/j.ijproman.2015.06.012
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Fig. 1 2D kernel density estimation approach. Every point in the dataset includes an individual kernel (left), all the individual kernels are aggregated to obtain a general density function (right). Based on http://en.wikipedia.org/wiki/Multivariate_kernel_density_estimation
Fig. 1 2D kernel density estimation approach. Every point in the dataset includes an individual kernel (left), all the individual kernels are aggregated to obtain a general density function (right). Based on http://en.wikipedia.org/wiki/Multivariate_kernel_density_estimationCC-BY-NC-ND · From the PDF
Fig. 2 The random forest algorithm generates ntree CART trees (T), each one trained from a bootstrapped sample X and using stochastically selected features at each split. For classification problems each tree of the forest (ensemble) votes to give a consensual class.
Fig. 2 The random forest algorithm generates ntree CART trees (T), each one trained from a bootstrapped sample X and using stochastically selected features at each split. For classification problems each tree of the forest (ensemble) votes to give a consensual class.CC-BY-NC-ND · From the PDF
Fig. 3 AON Network
Fig. 3 AON NetworkCC-BY-NC-ND · From the PDF
Fig. 4 2D probability density estimation obtained for the case study when the EV=50% of BAC. Point 1 represents a point within the expected variability ranges. Point 2 can be considered a warning situation, and the probability that this point is reached just by random is only 5%. Point 3 is a point out of range which previous approaches do not detect as anomalous.
Fig. 4 2D probability density estimation obtained for the case study when the EV=50% of BAC. Point 1 represents a point within the expected variability ranges. Point 2 can be considered a warning situation, and the probability that this point is reached just by random is only 5%. Point 3 is a point out of range which previous approaches do not detect as anomalous.CC-BY-NC-ND · From the PDF
Fig. 5 Comparison of triad methodology versus anomaly detection. Rectangles represent the 95% and 75% confidence intervals for time and cost using the triad methodology.
Fig. 5 Comparison of triad methodology versus anomaly detection. Rectangles represent the 95% and 75% confidence intervals for time and cost using the triad methodology.CC-BY-NC-ND · From the PDF
Fig. 6 Project analysis as a classification problem. Simulation results are labelled as over -run or not depending on the final simulation result (left). These datasets are used to fit classification models (right). These models give an estimation of the probability of over -cost (up) or delay (bottom). Boundary decisions represent points in which the probability of over -run is 0.5.
Fig. 6 Project analysis as a classification problem. Simulation results are labelled as over -run or not depending on the final simulation result (left). These datasets are used to fit classification models (right). These models give an estimation of the probability of over -cost (up) or delay (bottom). Boundary decisions represent points in which the probability of over -run is 0.5.CC-BY-NC-ND · From the PDF
Image 7
CC-BY-NC-ND · From the PDF
Fig. 8 Information obtained by the methodology integrated with the classical Earned Value Management graph. The picture on the top represents the expected over -cost, the probability of over -cost, p(OC) and the range of expected variability of the project for the given advancement of the project measured in terms of EV. Analogous measures are represented for the time in the second picture (bottom).
Fig. 8 Information obtained by the methodology integrated with the classical Earned Value Management graph. The picture on the top represents the expected over -cost, the probability of over -cost, p(OC) and the range of expected variability of the project for the given advancement of the project measured in terms of EV. Analogous measures are represented for the time in the second picture (bottom).CC-BY-NC-ND · From the PDF

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Metadata

Authors
Acebes, F., Pereda, M., Poza, D., Pajares, J., & Galán, J. M.
Affiliations
  • Universidad de Valladolid (ES)
  • Universidad de Burgos (ES)
Corresponding author
Acebes, Fernando
ORCID
Venue
International Journal of Project Management
Publisher
Elsevier BV
Published
2015-10
Volume
33
Issue
7
Pages
1597-1609
ISSN
0263-7863 (ISSN-L)
DOI
10.1016/j.ijproman.2015.06.012
OpenAlex
W958645160
Cited by
120
References
2
Citations by year
2026: 15 · 2025: 16 · 2024: 14 · 2023: 10 · 2022: 10 · 2021: 12 · 2020: 13 · 2019: 8 · 2018: 9 · 2017: 6 · 2016: 5 · 2015: 2
FWCI
8.22
Type
journal article
Language
en
Open access
Licence
Locations
  • International Journal of Project Management (publishedVersion)
  • Hispana (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • arXiv (Cornell University) (submittedVersion, cc-by-nc-nd)
  • LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion, other-oa)
  • RWTH Publications (RWTH Aachen) (submittedVersion)
  • Repositorio Institucional de la Universidad de Burgos (University of Burgos) (submittedVersion, other-oa)
  • UVaDOC UVaDOC University of Valladolid Documentary Repository (University of Valladolid) (submittedVersion, other-oa)
  • University of Valladolid Documentary Repository (University of Valladolid) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • Regional Government of Castile and Leon (Spain) (VA056A12-2)
  • Spanish Ministerio de Ciencia e Innovación (CSD2010-00034)
Topics
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Construction Project Management and Performance
  • Social Sciences › Economics, Econometrics and Finance › Finance › Capital Investment and Risk Analysis
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Resource-Constrained Project Scheduling
Keywords
earned value management, Monte Carlo simulation, anomaly detection, statistical learning, project risk analysis, classification and regression

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