The field of multi-stage stochastic programming provides a rich modelling framework to tackle a broad range of real-world decision problems. In order to numerically solve such programs - once they get reasonably large - the in¯nite-dimensional optimization problem has to be discretized. The stochastic optimization program generally consists of an optimization model and a stochastic model. In the multi-stage case the stochastic model is most commonly represented as a multi-variety stochastic process. The most common technique to calculate an useable discretization is to generate a scenario tree from the underlying stochastic process. Scenario tree generation is exampled by reviewing one specic algorithm based on multi-dimensional facility location applying backward stage wise clustering.