Monday, 16:15 - 16:40 h, Room: MA 550


Bita Analui
Multistage stochastic optimization problems under model ambiguity


A multistage stochastic optimization problem with uncertainty about the underlying model is considered. In this paper and for the first time we introduce and develop an approach that explicitly takes into account the ambiguity in probability model for the real world class of multistage stochastic optimization problems where the robustness of the decisions are highly expected. This is done by developing the concept of ambiguity of dynamic trees for multistage stochastic optimization problems incorporating the results from multistage distance. In the presence model ambiguity one approach is to study a set of possible models in which the true model sits. In this line, we define this set as an ε-radius (for the given ε) ball around a reference measure P with respect to a multistage distance d and therefore robustify the original problem by a worst case approach with respect to this ambiguity neighborhood. This way we analyze the sensitivity with respect to model changes. For implementation, we consider an optimization horizon with weekly discretization, the uncertainty is the random behavior of electricity spot prices.


Talk 3 of the invited session Mon.3.MA 550
"Stochastic optimization for electricity production and trading" [...]
Cluster 18
"Optimization in energy systems" [...]


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