  Multi-stage optimization under uncertainty techniques can be used to solve
long-term management problems. Although many optimization modeling language
extensions as well as computational environments have been proposed, the
acceptance of this technique is generally low, due to the inherent complexity
of the modeling and solution process. In this paper a simplification to
annotate multi-stage decision problems under uncertainty is presented - this
simplification contrasts with the common approach to create an extension on top
of an existing optimization modeling language. This leads to the definition of
meta models, which can be instanced in various programming languages. An
example using the statistical computing language R is shown.
