Stochastic Optimization Modeling and Quantitative Project Management May/June 2008 (vol. 25 no. 3) pp. 29-36
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/MS.2008.77
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. 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.
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 Usage of this product signifies your acceptance of the Terms of Use. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||