Probabilistic Assessment of Cost, Time, and Revenue in a Portfolio of Projects Using Stochastic Agent-Based Simulation
Publication: Journal of Construction Engineering and Management
Volume 144, Issue 5
Abstract
Unforeseen conditions and uncertainties typically surround construction projects during the execution phase. These uncertainties are the main reason for schedule delays and cost overruns. Stochastic simulation models have been developed to consider projects’ uncertainties and various probabilities. Although numerous studies have been conducted on causes and effects of uncertainties in individual projects, the uncertainties in a portfolio of projects have not been assessed. This study presents an agent-based simulation model developed to consider time and cost uncertainties in an owner’s portfolio of projects. The model can simulate different conditions and scenarios, taking into account costs inflation during projects’ execution periods. It contributes to the portfolio management body of knowledge by simulating the progress of a portfolio of projects against different scenarios, considering stochastic time, cost, and inflation rates. The simulation results assist owners to do probabilistic assessment and reduce uncertainties and risks related to time, cost, and revenue of their portfolio of projects and calculate the confidence level of their decisions and strategies. To demonstrate the capabilities of the model, an example portfolio containing 50 projects is modeled and different scenarios are evaluated. Results of the simulation indicate that the model works well and provides detailed information at the portfolio level and projects’ level. Fitting probability distributions to the results, it is seen that using the most likely numbers as deterministic parameters may lead to overestimating portfolio revenue and cost. The results indicate that using the portfolio stochastic agent-based simulation model can have significant effects on organizations’ predictions regarding their costs and revenues.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Data generated or analyzed during the study are available from the corresponding author by request. Information about the Journal’s data sharing policy can be found here: http://ascelibrary.org/doi/10.1061/%28ASCE%29CO.1943-7862.0001263.
References
AbouRizk, S. M., and Halpin, D. W. (1990). “Probabilistic simulation studies for repetitive construction processes.” J. Constr. Eng. Manage., 575–594.
Ahuja, H. N., and Nandakumar, V. (1985). “Simulation model to forecast project completion time.” J. Constr. Eng. Manage., 325–342.
Al-Battaineh, H., and AbouRizk, S. (2013). “Optimization of intermediate-level budget allocation using a genetic algorithm.” Int. J. Archit., Eng. Constr., 2(3), 144–157.
Barraza, G. A., Back, W. E., and Mata, F. (2004). “Probabilistic forecasting of project performance using stochastic S curves.” J. Constr. Eng. Manage., 25–32.
Chen, J., and Askin, R. G. (2009). “Project selection, scheduling and resource allocation with time dependent returns.” Eur. J. Oper. Res., 193(1), 23–34.
Crandall, K. C. (1976). “Probabilistic time scheduling.” J. Constr. Div., 102(CO3), 415–423.
Crandall, K. C. (1977). “Analysis of schedule simulations.” J. Constr. Div., 103(3), 387–394.
De Meyer, A., Loch, C. H., and Pich, M. T. (2002). “Managing project uncertainty: From variation to chaos.” MIT Sloan Manage. Rev., 43(2), 60.
Diekmann, J. E. (1983). “Probabilistic estimating: Mathematics and applications.” J. Constr. Eng. Manage., 297–308.
Du, J., Kim, B. C., and Zhao, D. (2016). “Cost performance as a stochastic process: EAC projection by Markov chain simulation.” J. Constr. Eng. Manage., 04016009.
El-Abbasy, M. S., Elazouni, A., and Zayed, T. (2017). “Generic scheduling optimization model for multiple construction projects.” J. Comput. Civ. Eng., 04017003.
Farshchian, M. M., Heravi, G., and AbouRizk, S. (2017). “Optimizing the owner’s scenarios for budget allocation in a portfolio of projects using agent-based simulation.” J. Constr. Eng. Manage., 04017022.
Firouzi, A., Yang, W., and Li, C. Q. (2016). “Prediction of total cost of construction project with dependent cost items.” J. Constr. Eng. Manage., 04016072.
Gabriel, S. A., Ordóñez, J. F., and Faria, J. A. (2006). “Contingency planning in project selection using multiobjective optimization and chance constraints.” J. Infrastruct. Syst., 112–120.
Gajpal, Y., and Elazouni, A. (2015). “Enhanced heuristic for finance-based scheduling of construction projects.” Constr. Manage. Econ., 33(7), 531–553.
Kishore, V., Abraham, D. M., and Sinfield, J. V. (2011). “Portfolio cash assessment using fuzzy systems theory.” J. Constr. Eng. Manage., 333–343.
Lee, D. E. (2005). “Probability of project completion using stochastic project scheduling simulation.” J. Constr. Eng. Manage., 310–318.
Li, X., Fang, S., Tian, Y., and Guo, X. (2015). “Expanded model of the project portfolio selection problem with divisibility, time profile factors and cardinality constraints.” J. Oper. Res. Soc., 66(7), 1132–1139.
Liao, C., and Barooah, P. (2011). “A novel stochastic agent-based model of building occupancy.” American Control Conf. (ACC), IEEE, San Francisco, 2095–2100.
Likhitruangsilp, V., and Ioannou, P. G. (2003). “Stochastic evaluation of tunneling performance using discrete-event simulation.” Construction Research Congress: Wind of Change: Integration and Innovation, ASCE, Reston, VA, 1–8.
Lipke, W. (2013). “Earned schedule—Ten years after.” Measurable News, 3(1), 15–21.
Love, P. E., Wang, X., Sing, C. P., and Tiong, R. L. (2012). “Determining the probability of project cost overruns.” J. Constr. Eng. Manage., 321–330.
Masoumi, R., and Touran, A. (2016). “A framework to form balanced project portfolios.” Construction Research Congress 2016, ASCE, Reston, VA, 1772–1781.
Nguyen, L. D., Phan, D. H., and Tang, L. C. M. (2013). “Simulating construction duration for multistory buildings with controlling activities.” J. Constr. Eng. Manage., 951–959.
PMI (Project Management Institute). (2013). A guide to the project management body of knowledge, 5th Ed., Newton Square, PA.
Rostami, M., Bagherpour, M., Mazdeh, M. M., and Makui, A. (2017). “Resource pool location for periodic services in decentralized multi-project scheduling problems.” J. Comput. Civ. Eng., 04017022.
Shojaei, A., and Flood, I. (2017). “Stochastic forecasting of unknown future project streams for strategic portfolio planning.” ASCE Int. Workshop on Computing in Civil Engineering, ASCE, Reston, VA, 280–288.
Sonmez, R., and Uysal, F. (2015). “Backward-forward hybrid genetic algorithm for resource-constrained multiproject scheduling problem.” J. Comput. Civ. Eng., 04014072.
Špačková, O., and Straub, D. (2013). “Dynamic Bayesian network for probabilistic modeling of tunnel excavation processes.” Comput.-Aided Civil Infrastruct. Eng., 28(1), 1–21.
Spooner, J. E. (1974). “Probabilistic estimating.” J. Constr. Div., 100(1), 65–77.
SPSS version 11.5 [Computer software]. IBM, Armonk, NY.
Taghaddos, H., Hermann, U., AbouRizk, S., and Mohamed, Y. (2014). “Simulation-based multiagent approach for scheduling modular construction.” J. Comput. Civ. Eng., 263–274.
Takama, T., and Preston, J. (2008). “Forecasting the effects of road user charge by stochastic agent-based modelling.” Transp. Res. Part A Policy Pract., 42(4), 738–749.
Tian, Y., Sun, M., Ye, Z., and Yang, W. (2016). “Expanded models of the project portfolio selection problem with loss in divisibility.” J. Oper. Res. Soc., 67(8), 1097–1107.
Touran, A. (2010). “Probabilistic approach for budgeting in portfolio of projects.” J. Constr. Eng. Manage., 361–366.
Touran, A. (2014). “A mathematical structure for modeling uncertainty in cost, schedule, and escalation factor in a portfolio of projects.” Construction Research Congress 2014, ASCE, Reston, VA, 1743–1751.
Van Dam K. H., Nikolic I., and Lukszo Z. (2012). Agent-based modelling of socio-technical systems, Vol. 9, Springer, Dordrecht, Netherlands.
Vergara, A. J., and Boyer, L. T. (1974). “Probabilistic approach to estimating and cost control.” J. Constr. Div., 100(4), 543–552.
Werner, M., and AbouRizk, S. (2015). “Simulation case study: Modelling distinct breakdown events for a tunnel boring machine excavation.” Winter Simulation Conf. (WSC), IEEE, San Francisco, 3234–3245.
Information & Authors
Information
Published In
Journal of Construction Engineering and Management
Volume 144 • Issue 5 • May 2018
Copyright
©2018 American Society of Civil Engineers.
History
Received: May 2, 2017
Accepted: Nov 15, 2017
Published online: Mar 14, 2018
Published in print: May 1, 2018
Discussion open until: Aug 14, 2018
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.