Monte Carlo Simulation for Cost Forecasting in the Green Building Project
Main Article Content
Abstract
Monte Carlo Simulation is a mathematical technique that generates random variables for modelling risk. This technique is suitable and benefits to the various client such as public and private sector to evaluate the costing prepared by the Quantity Surveyor. The methodology used is a qualitative approach consisting of a case study and document analysis. The result shows through Monte Carlo simulation, can predict the worst return from the accuracy of the estimation and given absolute confidence for project development.
Metrics
Article Details
License
Copyright (c) 2020 Faridah Muhamad Halil, Hafiszah Ismail, Mohamad Sufian Hasim, Halim Hashim
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
References
Ali Touran, R. L. (2006). Modelling Cost Escalation in Large Infrastructure Projects. Journal of Construction Engineering and Management, 853-860. DOI: https://doi.org/10.1061/(ASCE)0733-9364(2006)132:8(853)
An, S. H., & Kang, K. I. (2005). A study on predicting construction cost of apartment housing using experts' knowledge at the early stage of projects. Journal of Architectural Institute of Korea, 21(6), 81–88.
Attalla, M., &Hegazy, T. (2003). Prediction cost deviation in reconstruction project: Artificial neural networks versus regression. Journal of Construction Engineering and Management, ASCE, 129(4), 405–411. DOI: https://doi.org/10.1061/(ASCE)0733-9364(2003)129:4(405)
Balcombe, K.G. & Smith, L.E. (1999), “Refining the use of Monte Carlo techniques for risk analysis in project planning”, The Journal of Development Studies, 36(2), 113-135. DOI: https://doi.org/10.1080/00220389908422623
Bennett, J. & Ormerod, R.N. (1984), “Simulation applied to construction projects”, Construction Management and Economics, 2(3), 225-263 DOI: https://doi.org/10.1080/01446198400000021
Chen, G., 2013. Monte Carlo simulation of π and the discussion of variance reduction techniques. J. Convergence Inform. Technol., 8, 850-859. DOI: https://doi.org/10.4156/jcit.vol8.issue4.97
Choi, J., & Ryu, H.-G (2015). Statistical analysis of construction productivity for highway pavement operation. KSCE Journal of Civil Engineering. 19(5), 1193-1202. DOI: https://doi.org/10.1007/s12205-014-0425-2
Faris, K. R., & D. Patterson. (2007). Managing Risk in the Project Portfolio. Conference Paper, Newtown Square: Project Management Institute.
Ferry, D. J., & Brandon, P. S. (1979). Cost Planning of Building El-Sadek, A. (2010) Monte Carlo Approach to Developing a Water Quality Process- Factor. International Journal of Water Resources and Environmental Management, 1, 97-104.
Grinstead, C.M. & Snell, J.L. (2012), Introduction to Probability, American Mathematical Society, Providence. DOI: https://doi.org/10.1090/stml/057
Hertz, D. B. (1964). Risk Analysis in Capital Investment.
Hongxiang, C., & Wei, C. (2013). Uncertainty Analysis by Monte Carlo Simulation in a Life Cycle Assessment of Water-Saving Project in Green Buildings. Information Technology Journal, 12(13), 2593-2598. DOI: https://doi.org/10.3923/itj.2013.2593.2598
Jahangirian, M., Eldabi, T., Naseer, A., Stergioulas, L.K. & Young, T. (2010), “Simulation in manufacturing and business: a review”, European Journal of Operational Research, 203(1), 1-13. DOI: https://doi.org/10.1016/j.ejor.2009.06.004
Khedr, M.K. (2006), “Project risk management using Monte Carlo simulation”, 50th Annual Meeting, AACE International, Las Vegas, NV.
Kwak, Y.H. &Ingall, L. (2007), “Exploring Monte Carlo simulation applications for project management”, Risk Management, 9(1), 44-57. DOI: https://doi.org/10.1057/palgrave.rm.8250017
Liu, N. & Q. Zhang. (2012). Asymmetric stochastic volatility model estimation using improved markov chain Monte Carlo method. Journal Convergence Information Technology., 7, 179-186. DOI: https://doi.org/10.4156/jcit.vol7.issue16.21