Estimation of cost, duration, and technical performance activities is one of the most
difficult aspects of project management. Monte Carlo simulations are based on the manipulation of random numbers to evaluate probable outcomes, with applicability in a variety of different fields. By assigning probabilities, which can be determined a priori, to various events, it is possible to track the evolution of the system over length and time scales which are not normally accessible to other simulation techniques. Monte Carlo simulations can provide insights, which can be used to develop more realistic models. †
- Advanced Monte Carlo Methods: Direct Simulation, Prof. Dr. Michael Mascagni, Swiss Federal Institute of Technology, Zurich
- Advanced Monte Carlo Methods: General Principles of the Monte Carlo Method, Prof. Dr. Michael Mascagni, Swiss Federal Institute of Technology, Zurich
- Examining the Value of Monte Carlo Simulation for Project Time Management, Goran Avlijaš, Singidunum University, Belgrade, Serbia, Management: Journal of Sustainable Business and Management Solutions in Emerging Economies
- A Note on Early Monte Carlo Computations and Scientific Meetings, Cuthbert C. Hurd, IEEE Annals of the History of Computing, Volume 7, Issue 2.
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- Monte Carlo Methods: Early History and The Basics, Prof. Michael Mascagni, Department of Computer Science, Department of Mathematics, Department of Scientific Computing, Florida State University, Tallahassee, FL 32306 USA
- An Investigation In Construction Cost Estimation Using a Monte Carlo Simulation, Jeffery D. Bucholtz, Air Force Institute of Technology, 6-16-2016.
- Exploring Monte Carlo Simulation Applications for Project Management, Young Hoon Kwak and Lisa Ingall, IEEE Engineering Management Review, Vol. 37, No. 2 Second Quarter 2009.
- Risk Consideration and Cost Estimation in Construction Projects Using Monte Carlo Simulation, Claudius A. Peleskei, Vasile Dorca, and Radu A. Munteanu, Management 10(2), pp. 163-176
- The Benefits of Monte-Carlo Schedule Analysis, Mr. Jason Verschoor, 2005 AACE International Transactions
- A Combined Monte Carlo and Possibilistic Approach to Uncertainty Propagation in Event Tree Analysis, Piero Baraldi and Enrico Zio.
- Advance data mining for Monte Carlo simulation in project management, Sergio Sebastián Rodríguez.
- An effective methodology for the stochastic project compression problem, Gary Mitchell and Ted Klastorin, IEE Transactions, 26 Sep 2007.
- Bayesian Monte Carlo, Carl Edward Rasmussen and Zoubin Ghahramani
- Combining Monte Carlo Simulation and Bayesian Networks Methods for Assessing Completion Time of Projects under Risk, Ali Namazian, Siamak Haji Yakhchali, Vahidreza Yousefi, and Jolanta Tamošaitien, International Journal of Environmental Research and Public Health
- Dynamic Fault Tree analysis using Monte Carlo simulation in probabilistic safety assessment, Durga Rao Karanki, et al, Reliability Engineering and System Safety, 94(4), pp 874-883.
- Estimating projects duration in uncertain environments: Monte Carlo simulations strike back, Stefania Tattoni and Massimiliano M. Schiraldi, 22nd IPMA World Congress, “Project Management to Run” 9 - 11 November 2008, Roma, Italy.
- Estimating Total Program Cost of a LongTerm, High-Technology, High-Risk Project with Task Durations and Costs That May Increase Over Time, Dr. Gerald G. Brown, Maj Roger T. Grose, and DR. Robert A. Koyak, Military Operations Research, V11, N4, 2006
- Examining the Value of Monte Carlo Simulation for Project Time Management, Goran Avlijaš, Management: Journal of Sustainable Business and Management Solutions in Emerging Economies2019/24(1).
- Fuzzy Monte Carlo Simulation and Risk Assessment in Construction, N. Sadeghi and A. R. Fayek, Computer-Aided Civil and Infrastructure Engineering 25 (2010) 238–252
- Guiding Principles for Monte Carlo Analysis, EPA/630/R-97/001, March 1997
- Introduction To Monte Carlo Simulation, Robert L. Harrison, AIP Conf Proc. 2010 January 5; 1204: 17–21
- Introduction to Monte Carlo, Shirley Ho, Astro 542, Princeton University
- Modern Monte Carlo Methods for Efficient Uncertainty Quantification and Propagation: A Survey, Jiaxin Zhang
- Recent Advances and Future Prospects for Monte Carlo, Forrest B. Brown
- Stan Ulam, John Von Neumann, and the Monte Carlo Method, Roger Eckhardt, Los Alamos Science, Special Issue, 1987
- Recent Advances & Future Prospects for Monte Carlo, Forrest Brown, Senior Scientist, Monte Carlo Codes, XCP-3, Los Alamos National Laboratory
- Using Monte Carlo Simulation to Mitigate the Risk of Project Cost Overruns, Zakia Bouayed, International Journal of Safety and Security, Vol. 6, No.2, 2016
- Simulation for the masses: Spreadsheet-based Monte Carlo simulation, Thomas Schriber, Proceedings of the 2009 Winter Simulation Conference
- Probabilistic estimation of software project duration, New Zealand Journal of Applied Computing & Information Technology, 11(1), 11-22
- Estimating the Accuracy of the Return on Investment (ROI) Performance Evaluations, Alexei Botchkarev
† "Monte Carlo simulations as a route to compute probabilities," Parasuraman Swaminathan. Probabilistic Estimation of Software Project Duration, A.M. Connor, Auckland University of Technology, Interdisciplinary Journal of Information, Knowledge, and Management, 10, 217-233
