Schedule Risk Analysis Using Monte Carlo Simulation for Residential Projects.

  • Khalil Ismail Wali Department of Civil, College of Engineering, University of Salahaddin, Erbil, Kurdistan Region, Iraq
  • Shamal Ali Othman Department of Civil, College of Engineering, University of Salahaddin, Erbil, Kurdistan Region, Iraq
Keywords: Schedule Risk Analysis, The Project Evaluation, and Review Technique (PERT), Monte Carlo simulation, Critical Path Method (CPM), Risky Project

Abstract

Scheduling is an essential part of construction project management. Planning and scheduling of construction tasks help engineers to complete the project on time and within the budget. Most of the construction project failed to finish within planned duration; one of the reasons is regarded to estimated project duration without considering uncertainties that may cause a delay in performing specific activities. Hence it is vital to develop a risk management process which deals with the risks of execution that affects the project duration. This study focused on Schedule Risk Analysis Using Monte Carlo Simulation for Residential Projects, by taking the construction of a residential house as a case study.  The primary objective of this study analyzes the output of a project schedule risk simulation when Monte Carlo use to simulate the duration of individual activities of the project and compare the total project duration outputs graphically and through statistical analysis. Consequently, using the Critical Path Method (CPM) to determine the project duration, which is equal to 96 days.  For deciding the activity duration, the researcher has made a form. The form consists of all house tasks and estimated quantity with three columns for estimating Optimistic Duration, Most Likely Duration, and Pessimistic Duration in accordance with the respondent's perception for establishing the project duration by using the Program Evaluation and Review Techniques (PERT) method, which is equal 103 days. Project duration with low risk equal to 103 days, with base risks equal to 107 days and with high-risk project duration equal to 111 days. The outcomes clearly show that it is extremely unlikely to complete the project within 98 days and there is 100% chance that the project will be completed in 115 days. The sensitivity analysis for residential construction house indicates that the project schedule is most sensitive to the activity of “Wall Ceramic Tiles”, which can influence the completion date because of the correlation coefficient of this activity reached to 0.39 and top-ranked of all other activities.

References

5. References
BENNETT, F. L., LU, M. & ABOURIZK, S. 2001. Simplified CPM/PERT simulation model. Journal of construction engineering and management, 127, 513-514.
CARBONE, T. A. & TIPPETT, D. D. 2004. Project risk management using the project risk FMEA. Engineering Management Journal, 16, 28-35.
COOPER, D. F. 2005. Project risk management guidelines: Managing risk in large projects and complex procurements, John Wiley & Sons, Inc.
GANAME, P. & CHAUDHARI, P. 2015. Construction building schedule risk analysis using Monte-Carlo simulation. International Research Journal of Engineering and Technologi, 2, 1402-1406.
GUIDE, P. A guide to the project management body of knowledge. Project Management Institute, 2017.
HUANG, J.-W. & WANG, X.-X. Risk analysis of construction schedule based on PERT and MC simulation. Information Management, Innovation Management and Industrial Engineering, 2009 International Conference on, 2009. IEEE, 150-153.
HULETT, D. T. 1996. Schedule risk analysis simplified. Project Management Journal.
KERZNER, H. 2017. Project management: a systems approach to planning, scheduling, and controlling, John Wiley & Sons.
MADADI, M. & IRANMANESH, H. 2012. A management oriented approach to reduce a project duration and its risk (variability). European Journal of Operational Research, 219, 751-761.
MIZUNO, O., KIKUNO, T., TAKAGI, Y. & SAKAMOTO, K. Characterization of risky projects based on project managers' evaluation. Proceedings of the 22nd international conference on Software engineering, 2000. ACM, 387-395.
MUBARAK, S. 2010. Construction project scheduling and control. Wiley.
MUN, J. 2006. Modeling risk: Applying Monte Carlo simulation, real options analysis, forecasting, and optimization techniques, John Wiley & Sons.
NASIR, D., MCCABE, B. & HARTONO, L. 2003. Evaluating risk in construction–schedule model (ERIC–S): construction schedule risk model. Journal of construction engineering and management, 129, 518-527.
ÖKMEN, Ö. & ÖZTAŞ, A. 2008. Construction project network evaluation with correlated schedule risk analysis model. Journal of Construction Engineering and Management, 134, 49-63.
ROBERT, C. P., CASELLA, G. & CASELLA, G. 2010. Introducing Monte Carlo methods with r, Springer.
RUI-MEI, L. 2015. Properties of Monte Carlo and its application to risk management. International Journal of u-and e-Service, 8, 381-390.
SADEGHI, N., FAYEK, A. R. & PEDRYCZ, W. 2010. Fuzzy Monte Carlo simulation and risk assessment in construction. Computer‐Aided Civil and Infrastructure Engineering, 25, 238-252.
VAJJHALA, N. R. 2012. Microsoft Project as a Risk Management Tool. International Journal of Risk and Contingency Management (IJRCM), 1, 54-57.
VANHOUCKE, M. 2012. Project management with dynamic scheduling, Springer.
VISSER, J. K. Suitability of different probability distributions for performing schedule risk simulations in project management. Management of Engineering and Technology (PICMET), 2016 Portland International Conference on, 2016. IEEE, 2031-2039.
WOOD, D. A. 2002. Risk simulation techniques to aid project cost-time planning and management. Risk Management, 4, 41-60.
Published
2019-10-17
How to Cite
Ismail Wali, K., and S. Othman. “Schedule Risk Analysis Using Monte Carlo Simulation for Residential Projects.”. ZANCO Journal of Pure and Applied Sciences, Vol. 31, no. 5, Oct. 2019, pp. 90-3, doi:10.21271/zjpas.31.5.11.
Section
Mathematics ,Physics and Engineering Researches