Journal of Structural and Construction Engineering

Journal of Structural and Construction Engineering

Comparison of Importance Sampling Method with Other Probability Estimation Methods for Completion of Construction Projects

Document Type : Original Article

Authors
Mostafa Aliahmad 1
Mahmoud Miri 2
Mohsen Rashki 3
1 Ph.D candidate, Department of Civil Engineering, University of Sistan and Baluchestan, Zahedan, Iran.
2 Professor, Department of Civil Engineering, University of Sistan and Baluchestan, Zahedan, Iran
3 Assistant Professor, Department of Architecture Engineering, University of Sistan and Baluchestan, Zahedan, Iran
10.22065/jsce.2023.403524.3158
Abstract
The Program Evaluation and Review Technique (PERT) is a stochastic network that utilizes the Beta-PERT distribution function. It was developed in the late 1950s to account for uncertainty in project management. However, due to simplifying assumptions and optimistic estimates resulting from considering the critical path alone, this method has been criticized by many researchers. New methods have been introduced that are believed to better model the actual completion time of projects. In this study, the Importance Sampling (IS) method was used for managing construction projects. Reliability methods allow for any desired distribution function to be considered for activity times, and all available paths for project completion can be taken into account using reliability systems. Five common numerical examples were investigated using importance sampling, Monte Carlo Simulation (MCS), First-Order Reliability Method (FORM), and PERT. The probability of failure (defined here as the probability of not completing the project within a certain deadline) was compared. The results indicated a significant difference in the results of the PERT and first-order reliability method compared to Monte Carlo simulation and importance sampling. Additionally, the importance sampling method had the least difference (17%) among the studied methods compared to Monte Carlo simulation. Furthermore, this study shows that Monte Carlo simulation and importance sampling methods are more reliable for accurately estimating the probability of project completion compared to PERT and first-order reliability methods.
Keywords
Reliability
Project management
Monte Carlo simulation
Importance sampling
Project evaluation and review technique
First order reliability method

Subjects

[1] Mouhoub N.E, Benhocine A., Belouadah H. (2011).  A new method for constructing a minimal PERT network, Applied Mathematical Modelling, 35, 4575–4588.
[2] W.A. Haga W.A., Tim O’keefe T. (2001). Crashing PERT networks: a simulation approach, in: 4th International Conference of the Academy of Business and Administrative Sciences, Quebec City, Canada, July 12–14.
[3] Malcolm D. G, Roseboom J. H, Clark C. E, Fazar W. (1959). Application of a technique for research and development program evaluation, Operations Research, 7(5), 646-669.
[4] Azaron A., Perkgoz C., Sakawa M. (2005). A genetic algorithm approach for the time-cost trade-off in PERT networks, Applied Mathematics and Computation, 168, 1317–1339.
[5] Bordley R.F., Keisler J.M., Logan T.M. (2019). Managing Projects with Uncertain Deadlines, European Journal of Operational Research, Vol. 274(1), 291-302.
[6] Abdallah H., Emara H. M., Dorrah H. T., Bahgat A. (2009). Using Ant Colony Optimization algorithm for solving project management problems, Expert Systems with Applications, 36, 10004–10015.
[7] Hajdu M. (2013). Effects of the application of activity calendars on the distribution of project duration in PERT networks, Automation in Construction, 35, 397–404.
[8] Banerjee A., Paul A. (2008). On path correlation and PERT bias, European Journal of Operational Research, 189, 1208–1216.
[9] Ang, A.H.-S., Abdelnour, J. and Chaker, A.A. (1975). Analysis of Activity Networks Under Uncertainty, Journal of The Engineering Mechanics Division, Proceedings of the American Society of Civil Engineers, 101, 373-387.
[10] Winston, W.L. (2003). Operations Research: Applications & Algorithms, in: 4th ed. Thomson Business Press, (2003).
[11] AbouRizk, S. (2010). Role of simulation in construction engineering and management, Journal of Construction Engineering and Management, 136(10), 1140–53.
[12] Malyusz L., Hajdu M., Vattai Z. (2021). Comparison of different algorithms for time analysis for CPM schedule networks, Automation in Construction, 127.
[13] Van Slyke R.M. (1963). Monte Carlo Methods and the PERT Problem. Operations Research, 10(5), 839-860.
[14] McGowan, L.L, (1964). Monte Carlo Techniques Applied to PERT Networks, MS thesis: Texas A&M University, College Station, Texas.
[15] Diaz, C.F, (1989). Probabilistic Network Analyses for Construction Projects”, MS thesis: Ohio State University, Columbus, Ohio.
[16] Hartley, H.O., Wortham, A.W. (1966). A Statistical Theory for PERT Critical Path Analysis, Management Science, 12(10), B469-B481.
[17] Ringer, L.J. (1969). Numerical Operators for Statistical PERT Critical Path Analysis”, Management Science, 16(2).
[18] Nowak, S.A., Collins, K.R. (2000). Reliability of Structures, McGraw-Hill: New York.
[19] Keshtegar, B. (2016). Chaotic conjugate stability transformation method for structural reliability analysis”, Computer Methods in Applied Mechanics and Engineering, 310, 866–885.
[20] Metropolis, N., Ulam, S. (1949). The Monte Carlo Method”, Journal of the American Statistical Association, 44, 335-41.
[21] Rashki, M. (2014). A new and efficient simulation method for sensitivity analysis and reliability based design of structures, Ph.D thesis: University of Sistan and Baluchestan, Zahedan, Iran.
[22] Perez J.G., Martin M.M.L., Garcia C.G., Angel M., Granero S. (2016). Project management under uncertainty beyond beta: the generalized bicubic distribution, Operations Research Perspectives, 3, 67-76.
[23] Davis R. (2008). Teaching Note—Teaching Project Simulation in Excel Using PERT-Beta Distributions. INFORMS Transactions on Education, 8(3):139-148.
[24] Andrie Pasca Hendradewa A.P. (2019). Schedule Risk Analysis by Different Phases of Construction Project Using CPM-PERT and Monte-Carlo Simulation, IOP Conf. Series: Materials Science and Engineering, 528.

  • Receive Date 15 July 2023
  • Revise Date 26 August 2023
  • Accept Date 23 September 2023