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Issue No.03 - May/June (2008 vol.25)
pp: 29-36
Uma Sudhakar Rao , Unisys Global Services India
Srikanth Kestur , Unisys Global Services India
Chinmay Pradhan , Unisys Global Services India
ABSTRACT
A successful project effectively manages four cornerstones—schedule, cost, scope, and quality—to achieve its goals. Every project activity influences these four cornerstones. Stochastic optimization modeling factors in the uncertainties associated with project activities and provides insight into the expected project outputs as probability distributions rather than as deterministic approximations. Integrating stochastic optimization modeling with quantitative project management provides near-real-time feedback, enabling projects to monitor areas that induce maximum variability and thereby initiate corrective actions as required.
INDEX TERMS
stochastic optimization modeling, quantitative project management, monte carlo simulations, process capability baselines, sensitivity analysis, SWOT analysis
CITATION
Uma Sudhakar Rao, Srikanth Kestur, Chinmay Pradhan, "Stochastic Optimization Modeling and Quantitative Project Management", IEEE Software, vol.25, no. 3, pp. 29-36, May/June 2008, doi:10.1109/MS.2008.77
REFERENCES
1. K.G. Murthy, Optimization Models for Decision Making: Volume 1 (Junior Level), Dept. of Industrial and Operations Eng., Univ. of Michigan, 2003, http://ioe.engin.umich.edu/people/fac/books/ murtyopti_model.
2. M.B. Chrissis, M. Konrad, and S. Shrum, CMMI, 2nd ed., Addison-Wesley, 2007.
3. R. Perry, "Applications of Monte Carlo Simulation in Design for Six Sigma," 2006 Crystal Ball User Conf., Oracle, 2006, www.crystalball.com/cbuc/2006/paperscbuc06-perry.pdf .
4. E. Herrera, "Stochastic Critical Path," 2006 Crystal Ball User Conf., Oracle, 2006, www.crystalball.com/cbuc/2006/paperscbuc06-herrera.pdf .
5. Crystal Ball 7.3 User Manual, Oracle, 2007.
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