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Open AccessArticle

Forecasting the Project Duration Average and Standard Deviation from Deterministic Schedule Information

1
Escuela Superior de Ingeniería, Universidad de Cádiz, 11519 Puerto Real (Cádiz), Spain
2
School of Economics and Management, Chang’an University, Xi’an 710064, China
3
Department of Business Informatics and Operations Management, Ghent University, 9000 Gent, Belgium
4
Vlerick Business School, 9000 Gent, Belgium
5
UCL School of Management, University College London, London E14 5AA, UK
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2020, 10(2), 654; https://doi.org/10.3390/app10020654
Received: 10 November 2019 / Revised: 19 December 2019 / Accepted: 13 January 2020 / Published: 16 January 2020
Most 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.
Keywords: project duration; scheduling; merge event bias; construction; PERT project duration; scheduling; merge event bias; construction; PERT
MDPI and ACS Style

Ballesteros-Pérez, P.; Cerezo-Narváez, A.; Otero-Mateo, M.; Pastor-Fernández, A.; Zhang, J.; Vanhoucke, M. Forecasting the Project Duration Average and Standard Deviation from Deterministic Schedule Information. Appl. Sci. 2020, 10, 654.

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