A Bayesian network framework for project cost, benefit and risk analysis with an agricultural development case study

cg.contributor.crpWater, Land and Ecosystems
cg.identifier.doihttps://doi.org/10.1016/j.eswa.2016.05.005en
cg.identifier.wlethemeDecision Analysis and Informationen
cg.issn0957-4174en
cg.journalExpert Systems with Applicationsen
cg.volume60en
dc.contributor.authorYet, Barbarosen
dc.contributor.authorConstantinou, Anthonyen
dc.contributor.authorFenton, Normanen
dc.contributor.authorNeil, Martinen
dc.contributor.authorLuedeling, Eikeen
dc.contributor.authorShepherd, Keith D.en
dc.date.accessioned2016-05-23T10:23:33Zen
dc.date.available2016-05-23T10:23:33Zen
dc.identifier.urihttps://hdl.handle.net/10568/74285
dc.titleA Bayesian network framework for project cost, benefit and risk analysis with an agricultural development case studyen
dcterms.abstractSuccessful implementation of major projects requires careful management of uncertainty and risk. Yet such uncertainty is rarely effectively calculated when analysing project costs and benefits. This paper presents a Bayesian Network (BN) modelling framework to calculate the costs, benefits, and return on investment of a project over a specified time period, allowing for changing circumstances and trade-offs. The framework uses hybrid and dynamic BNs containing both discrete and continuous variables over multiple time stages. The BN framework calculates costs and benefits based on multiple causal factors including the effects of individual risk factors, budget deficits, and time value discounting, taking account of the parameter uncertainty of all continuous variables. The framework can serve as the basis for various project management assessments and is illustrated using a case study of an agricultural development project.en
dcterms.accessRightsLimited Access
dcterms.bibliographicCitationYet, Barbaros;Constantinou, Anthony;Fenton, Norman;Neil, Martin;Luedeling, Eike;Shepherd, Keith.2016.A Bayesian network framework for project cost, benefit and risk analysis with an agricultural development case study.Elsevier.doi:10.1016/j.eswa.2016.05.005en
dcterms.extentpp. 141-155en
dcterms.issued2016-10en
dcterms.languageen
dcterms.licenseCopyrighted; all rights reserved
dcterms.publisherElsevieren
dcterms.subjectproject managementen
dcterms.subjectrisk analysisen
dcterms.subjectcost-benefit analysisen
dcterms.subjectbayesian networksen
dcterms.typeJournal Article

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