Optimization problems where some parameters are not known with certainty in advance are called stochastic programming problems. In order to be able to solve such problems, assumptions about the probability distribution of the unknown parameters must be made. Depending on these assumptions and the objective, different types of stochastic programming models arise, see f.ex the online Stochastic Programming Introduction
We will ONLY consider models which are:
- with (multistage) recourse
- with finite distribution (aka scenario based)
Allthough this might seem rather restrictive, most applied problems fall into this category.