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dc.contributor.authorSantos Martín, José Ignacio 
dc.contributor.authorPereda, María
dc.contributor.authorAhedo García, Virginia 
dc.contributor.authorGalán Ordax, José Manuel 
dc.date.accessioned2023-05-03T08:29:25Z
dc.date.available2023-05-03T08:29:25Z
dc.date.issued2023-06
dc.identifier.issn0360-8352
dc.identifier.urihttp://hdl.handle.net/10259/7665
dc.description.abstractProject control is a crucial phase within project management aimed at ensuring —in an integrated manner— that the project objectives are met according to plan. Earned Value Management —along with its various refinements— is the most popular and widespread method for top-down project control. For project control under uncertainty, Monte Carlo simulation and statistical/machine learning models extend the earned value framework by allowing the analysis of deviations, expected times and costs during project progress. Recent advances in explainable machine learning, in particular attribution methods based on Shapley values, can be used to link project control to activity properties, facilitating the interpretation of interrelations between activity characteristics and control objectives. This work proposes a new methodology that adds an explainability layer based on SHAP —Shapley Additive exPlanations— to different machine learning models fitted to Monte Carlo simulations of the project network during tracking control points. Specifically, our method allows for both prospective and retrospective analyses, which have different utilities: forward analysis helps to identify key relationships between the different tasks and the desired outcomes, thus being useful to make execution/replanning decisions; and backward analysis serves to identify the causes of project status during project progress. Furthermore, this method is general, model-agnostic and provides quantifiable and easily interpretable information, hence constituting a valuable tool for project control in uncertain environments.en
dc.format.mimetypeapplication/pdf
dc.format.mimetypeimage/jpeg
dc.language.isoenges
dc.publisherElsevieren
dc.relation.ispartofComputers & Industrial Engineering. 2023, V. 180, 109261en
dc.rightsAtribución 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectProject managementen
dc.subjectStochastic project controlen
dc.subjectEarned value managementen
dc.subjectShapley valuesen
dc.subjectExplainable machine learningen
dc.subjectSHAPen
dc.subject.otherEmpresas-Gestiónes
dc.subject.otherIndustrial managementen
dc.subject.otherIngenieríaes
dc.subject.otherEngineeringen
dc.subject.otherInformáticaes
dc.subject.otherComputer scienceen
dc.titleExplainable machine learning for project management controlen
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.relation.publisherversionhttps://doi.org/10.1016/j.cie.2023.109261es
dc.identifier.doi10.1016/j.cie.2023.109261
dc.journal.titleComputers & Industrial Engineeringen
dc.volume.number180es
dc.page.initial109261es
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones


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