Decision makers often have to make decisions in the presence of uncertainty.
Decision problems are often formulated as optimization problems, and thus in many situations decision makers wish to solve optimization problems which depend on parameters which are unknown.
Typically it is quite difficult to formulate and solve such problems, both conceptually and numerically.
The difficulty already starts at the conceptual stage of modeling.
Usually there are a variety of ways in which the uncertainty can be formalized.
In the formulation of optimization problems, one usually attempts to find a good trade-off between the realism of the optimization model, which usually affects the usefulness and quality of the obtained decisions, and the
tractability of the problem, so that it could be solved analytically or numerically.
As a result of these considerations there are a large number of different approaches for formulating and solving optimization problems under uncertainty.
It is impossible to give a complete survey of all such methods in one article. Therefore we rather aim this article at giving a flavor of prominent approaches to optimization under uncertainty.