Abstract
The ¯eld 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-variate
stochastic process. The most common technique to calculate an useable
discretization is to generate a scenario tree from the underlying sto-
chastic process. Scenario tree generation is exampli¯ed by reviewing one
speci¯c algorithm based on multi-dimensional facility location applying
backward stagewise clustering.
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-variate
stochastic process. The most common technique to calculate an useable
discretization is to generate a scenario tree from the underlying sto-
chastic process. Scenario tree generation is exampli¯ed by reviewing one
speci¯c algorithm based on multi-dimensional facility location applying
backward stagewise clustering.
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Algorithms for Optimization with Incomplete Information |
| Herausgeber*innen | Susanne Albers and Rolf H. Möhring and Georg Ch. Pflug and Rüdiger Schultz |
| Erscheinungsort | Volume 05031 of Dagstuhl Seminar Proceedings |
| Verlag | Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing |
| Seiten | 61 - 63 |
| Band | 05031 |
| Publikationsstatus | Veröffentlicht - 1 Dez. 2005 |