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Paz, J, C, Rozenboim, D, Cuadros, Cano, A, S. Escobar P, J, Juan Camilo Paz, David Rozenboim, Álvaro Cuadros, Sandra Cano, John Escobar (2018) A Simulation-Based Scheduling Methodology for Construction Projects Considering the Potential Impacts of Delay Risks . Construction Economics and Building, 18(02), 41-69.
- Type: Journal Article
- Keywords: Construction, Monte Carlo simulation, project management, risk analysis, scheduling
- ISBN/ISSN: 2204-9029
- URL: https://doi.org/10.5130/AJCEB.v18i2.5842
This paper tackles the problem of scheduling construction projects considering the influence of delay risks. In the actual body of knowledge, several methods have been proposed to handle this problem, starting from the Project Evaluation and Review Technique to advanced simulation models. However, this investigation proposes a novel integration of one methodology with some approaches already existing in the literature related to Monte Carlo Simulation scheduling techniques as seen from the perspective of a practitioner. The research began with a literature review of both schedule risks and Monte Carlo based scheduling models for construction projects. Based on this, the methodology was designed with the constant participation of experts in the construction industry. As result of this, a comprehensive and practical methodology was constructed. Therefore, a new mathematical structure for the simulation model within the methodology was formulated in which a new concept for each risk defined as “potential impact” was used. Moreover, the simulation model is based on the judgment of experts and methods of the known literature such as the explicit model of the occurrence probability of the risks and the activity-risk factor matrix. Then, to validate the tool, the proposed methodology was applied using the information of an already constructed construction project of a public university of Colombia. The obtained results were a confidence-based forecast of the end date of the project and a quantitative importance measure of the modelled risks. These results were compared against the real history of the project since it was found an excellent performance of the proposed methodology. To sum up, the research process described above supports the validity of the proposed methodology.