Genetic Algorithms for Scenario Generation in Stochastic Programming: Motivation and General Framework
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F08%3APU77910" target="_blank" >RIV/00216305:26210/08:PU77910 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Genetic Algorithms for Scenario Generation in Stochastic Programming: Motivation and General Framework
Original language description
Stochastic programs have been developed as useful tools for modeling of various application problems. The developed algorithms usually require a solution of large-scale linear and nonlinear programs because the deterministic reformulations of the original stochastic programs are based on empirical or sampling discrete probability distributions, i.e. on so-called scenario sets. The scenario sets are often large, so the reformulated programs must be solved. Therefore, the suitable scenario set generationtechniques are required. Hence, randomly selected reduced scenario sets are often employed. Related confidence intervals for the optimal objective function values have been derived and are often presented as tight enough. However, there is also demand for goal-oriented scenario generation to learn more about the extreme cases. Traditional deterministic max-min and min-min techniques are significantly limited by the size of scenario set. Therefore, this text introduces a general framework
Czech name
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Czech description
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Classification
Type
C - Chapter in a specialist book
CEP classification
BB - Applied statistics, operational research
OECD FORD branch
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Result continuities
Project
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Continuities
Z - Vyzkumny zamer (s odkazem do CEZ)
Others
Publication year
2008
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Book/collection name
Lecture Notes in Electrical Engineering, book series: Advances in Computational Algorithms and Data Analysis, Vol. 14 Ao, S.L., Rieger, B., Chen, S.S. (Eds.).
ISBN
978-1-4020-8918-3
Number of pages of the result
9
Pages from-to
527-536
Number of pages of the book
588
Publisher name
Springer
Place of publication
Netherlands
UT code for WoS chapter
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