Choice of Probability Distributions for Activity Durations in Project Networks with Limited Sample Size
Publication: Journal of Construction Engineering and Management
Volume 150, Issue 3
Abstract
Accurate estimation of activity durations is a critical project scheduling step. If abundant data are available for activity durations, then distribution fitting is straightforward. However, if sample sizes are small, then choosing an appropriate theoretical distribution for activity duration is challenging. In this study, we propose a methodology for experimenting with the impact of sample size on the accuracy of the estimated duration of a stochastic project network. We synthesized a total of 3,264 stochastic project networks and estimated the true project duration distribution for each network using large-sample Monte Carlo simulation. We varied the sample size per activity and investigated how the choice of theoretical distribution (normal, beta, triangular, and lognormal) for estimated activity durations impacted the accuracy of the estimated project duration distribution. We find that the best choice for a theoretical activity duration distribution highly depends on the sample size. For sample sizes below 50, we find that not much is gained from the common practice of putting extensive effort into selecting best-fit theoretical distributions compared to simply using normal distributions.
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Data Availability Statement
All data and codes generated and used during the study are available in an online repository at (Sadeghi 2022).
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Published In
Journal of Construction Engineering and Management
Volume 150 • Issue 3 • March 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Mar 10, 2023
Accepted: Oct 16, 2023
Published online: Jan 5, 2024
Published in print: Mar 1, 2024
Discussion open until: Jun 5, 2024
ASCE Technical Topics:
- Business management
- Construction engineering
- Construction management
- Distribution functions
- Engineering fundamentals
- Management methods
- Mathematical functions
- Mathematics
- Methodology (by type)
- Monte Carlo method
- Numerical methods
- Practice and Profession
- Probability
- Probability distribution
- Project management
- Scheduling
- Stochastic processes
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