A Methodology for Project Risk Analysis using Bayesian Belief Networks within a Monte Carlo Simulation Environment

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dc.contributor.advisorBaecher, Gregory Ben_US
dc.contributor.authorOrdonez Arizaga, Javier F.en_US
dc.contributor.departmentCivil Engineeringen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2007-06-22T05:36:00Z
dc.date.available2007-06-22T05:36:00Z
dc.date.issued2007-04-26
dc.description.abstractProjects are commonly over budget and behind schedule, to some extent because uncertainties are not accounted for in cost and schedule estimates. Research and practice is now addressing this problem, often by using Monte Carlo methods to simulate the effect of variances in work package costs and durations on total cost and date of completion. However, many such project risk approaches ignore the large impact of probabilistic correlation on work package cost and duration predictions. This dissertation presents a risk analysis methodology that integrates schedule and cost uncertainties considering the effect of correlations. Current approaches deal with correlation typically by using a correlation matrix in input parameters. This is conceptually correct, but the number of correlation coefficients to be estimated grows combinatorially with the number of variables. Moreover, if historical data are unavailable, the analyst is forced to elicit values for both the variances and the correlations from expert opinion. Most experts are not trained in probability and have difficulty quantifying correlations. An alternative is the integration of Bayesian belief networks (BBN's) within an integrated cost-schedule Monte Carlo simulation (MCS) model. BBN's can be used to implicitly generate dependency among risk factors and to examine non-additive impacts. The MCS is used to model independent events, which are propagated through BBN's to assess dependent posterior probabilities of cost and time to completion. BBN's can also include qualitative considerations and project characteristics when soft evidence is acquired. The approach builds on emerging methods of systems reliability.en_US
dc.format.extent1826993 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/1903/6871
dc.language.isoen_US
dc.subject.pqcontrolledEngineering, Civilen_US
dc.subject.pqcontrolledEngineering, System Scienceen_US
dc.subject.pquncontrolledRisk Analysisen_US
dc.subject.pquncontrolledProject Managementen_US
dc.subject.pquncontrolledBayesian Networksen_US
dc.titleA Methodology for Project Risk Analysis using Bayesian Belief Networks within a Monte Carlo Simulation Environmenten_US
dc.typeDissertationen_US

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