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dc.contributor.authorBallesteros-Pérez, Pablo
dc.contributor.authorCerezo-Narváez, Alberto
dc.contributor.authorOtero-Mateo, Manuel
dc.contributor.authorPastor-Fernández, Andrés
dc.contributor.authorZhang, Jingxiao
dc.contributor.authorVanhoucke, Mario
dc.date.accessioned2020-01-30T10:17:16Z
dc.date.available2020-01-30T10:17:16Z
dc.date.issued2020en_US
dc.identifier.doi10.3390/app10020654
dc.identifier.urihttp://hdl.handle.net/20.500.12127/6429
dc.description.abstractMost construction managers use deterministic scheduling techniques to plan construction projects and estimate their duration. However, deterministic techniques are known to underestimate the project duration. Alternative methods, such as Stochastic Network Analysis, have rarely been adopted in practical contexts as they are commonly computer-intensive, require extensive historical information, have limited contextual/local validity and/or require skills most practitioners have not been trained for. In this paper, we propose some mathematical expressions to approximate the average and the standard deviation of a project duration from basic deterministic schedule information. The expressions’ performance is successfully tested in a 4100-network dataset with varied activity durations and activity durations variability. Calculations are quite straightforward and can be implemented manually. Furthermore, unlike the Project Evaluation and Review Technique (PERT), they allow drawing inferences about the probability of project duration in the presence of several critical and subcritical paths with minimal additional calculation.en_US
dc.language.isoenen_US
dc.publisherMDPI AGen_US
dc.subjectProject Durationen_US
dc.subjectSchedulingen_US
dc.subjectMerge Event Biasen_US
dc.subjectConstructionen_US
dc.subjectPERTen_US
dc.titleForecasting the project duration average and standard deviation from deterministic schedule informationen_US
dc.identifier.journalApplied Sciencesen_US
dc.source.volume10en_US
dc.source.issue2en_US
dc.source.beginpage654en_US
dc.source.endpage676en_US
dc.contributor.departmentEscuela Superior de Ingeniería, Universidad de Cádiz, 11519 Puerto Real (Cádiz), Spainen_US
dc.contributor.departmentSchool of Economics and Management, Chang’an University, Xi’an 710064, Chinaen_US
dc.contributor.departmentGhent Universityen_US
dc.contributor.departmentUCL School of Management, University College London, Londonen_US
dc.identifier.eissn2076-3417
vlerick.knowledgedomainOperations & Supply Chain Managementen_US
vlerick.typearticleJournal article with impact factoren_US
vlerick.vlerickdepartmentTOMen_US
dc.identifier.vperid58614en_US


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