Although stochastic programming is a powerful tool for modeling decision-making under uncertainty, various impediments have historically prevented its wide-spread use. One key factor involves the ability of non-specialists to easily express stochastic programming problems as extensions of deterministic models, which are often formulated first. A second key factor relates to the difficulty of solving stochastic programming models, particularly the general mixed-integer, multi-stage case. Intricate, configurable, and parallel decomposition strategies are frequently required to achieve tractable run-times.

In this study, Professor David Woodruff and co-authors William Hart and Jean-Paul Watson from Sandia National Laboratories simultaneously address both of these factors in our PySP software package, which is part of the COIN-OR Coopr open-source Python project for optimization.