In the case of flooding, for example, the stochastic programming model would use various flood scenarios, resource supply capabilities at different dispatch locations, and fixed and variable costs associated with deployment of various flood-management resources to manage various risk measures. By assigning probabilities to the factors driving outcomes, the model outlines how limited resources can meet tomorrow's unknown demands or liabilities. In this way, the risks and rewards of various tradeoffs can be explored.
Stochastic programming offers greater modeling power and flexibility, but it comes at a cost-premium processing time. However, recently, stochastic programming has benefited from the development of more efficient algorithms and faster computer processors. This means that rather than predicting a limited future using forecasting, decisions supporting a wide range of probable scenarios can be taken. The model allows all unforeseen challenges to be solved, mostly within an hour, and has scalability that promises to manage even larger models in the future.
"What we have been able to accomplish is to make such innovative optimization solutions accessible and affordable to a wide spectrum of clients operating in diverse socio-economic environments," commented Tarun Kumar, an optimization researcher at IBM's T.J. Watson Research Center in Yorktown Heights, N.Y.
As stochastic models become more sophisticated, researchers like IBM's Dr. Gyana Parija have been able to infuse the models with "human" factors, such as politics, custom and culture. As researchers factor in human behavior in the models, the results grow less uncertain and more accurate and acceptable.