Logo of Humboldt-Universität zu BerlinLogo of Humboldt-Universität zu Berlin
edoc-Server
Open-Access-Publikationsserver der Humboldt-Universität
de|en
Header image: facade of Humboldt-Universität zu Berlin
View Item 
  • edoc-Server Home
  • Elektronische Zeitschriften
  • Stochastic Programming E-print Series (SPEPS)
  • Volume 2014
  • View Item
  • edoc-Server Home
  • Elektronische Zeitschriften
  • Stochastic Programming E-print Series (SPEPS)
  • Volume 2014
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.
All of edoc-ServerCommunity & CollectionTitleAuthorSubjectThis CollectionTitleAuthorSubject
PublishLoginRegisterHelp
StatisticsView Usage Statistics
All of edoc-ServerCommunity & CollectionTitleAuthorSubjectThis CollectionTitleAuthorSubject
PublishLoginRegisterHelp
StatisticsView Usage Statistics
View Item 
  • edoc-Server Home
  • Elektronische Zeitschriften
  • Stochastic Programming E-print Series (SPEPS)
  • Volume 2014
  • View Item
  • edoc-Server Home
  • Elektronische Zeitschriften
  • Stochastic Programming E-print Series (SPEPS)
  • Volume 2014
  • View Item
2014-05-07Buch DOI: 10.18452/8441
On Distributionally Robust Multiperiod Stochastic Optimization
Analui, Bita
Pflug, Georg Ch.
This paper considers model uncertainty for multistage stochastic programs. The data and information structure of the baseline model is a tree, on which the decision problem is defined. We consider ambiguity neighborhoods around this tree as alternative models which are close to the baseline model. Closeness is defined in terms of a distance for probability trees, called the nested distance. This distance is appropriate for scenario models of multistage stochastic optimization problems as was demonstrated in (Pflug and Pichler, 2012). The ambiguity model is formulated as a minimax problem, where the optimal decision is to be found, which minimizes the maximal objective function, within the ambiguity set. We give a setup for studying saddle point properties of the minimax problem. Moreover, we present solution algorithms for finding the minimax decisions at least asymptotically. As an example, we consider a multiperiod stochastic production/inventory control problem with weekly ordering. The stochastic scenario process is given by the random demands for two products. We find the worst trees within the ambiguity set and determine a solution which is robust w.r.t. model uncertainty. It turns out that the probability weights of the worst case trees are concentrated on few very bad scenarios.
Files in this item
Thumbnail
3.pdf — Adobe PDF — 618.5 Kb
MD5: e40a32530d8f18d47b5458c1ad6c980e
Cite
BibTeX
EndNote
RIS
InCopyright
Details
DINI-Zertifikat 2019OpenAIRE validatedORCID Consortium
Imprint Policy Contact Data Privacy Statement
A service of University Library and Computer and Media Service
© Humboldt-Universität zu Berlin
 
DOI
10.18452/8441
Permanent URL
https://doi.org/10.18452/8441
HTML
<a href="https://doi.org/10.18452/8441">https://doi.org/10.18452/8441</a>