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Statements

Subject Item
n2:RIV%2F00216305%3A26210%2F08%3APU77910%21RIV12-MSM-26210___
rdf:type
n12:Vysledek skos:Concept
dcterms: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 generation techniques 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 how to generate 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 generation techniques 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 how to generate
dcterms:title
Genetic Algorithms for Scenario Generation in Stochastic Programming: Motivation and General Framework Genetic Algorithms for Scenario Generation in Stochastic Programming: Motivation and General Framework
skos:prefLabel
Genetic Algorithms for Scenario Generation in Stochastic Programming: Motivation and General Framework Genetic Algorithms for Scenario Generation in Stochastic Programming: Motivation and General Framework
skos:notation
RIV/00216305:26210/08:PU77910!RIV12-MSM-26210___
n3:aktivita
n13:Z
n3:aktivity
Z(MSM0021630529)
n3:dodaniDat
n4:2012
n3:domaciTvurceVysledku
n15:6841074 n15:1286943
n3:druhVysledku
n17:C
n3:duvernostUdaju
n9:S
n3:entitaPredkladatele
n11:predkladatel
n3:idSjednocenehoVysledku
369097
n3:idVysledku
RIV/00216305:26210/08:PU77910
n3:jazykVysledku
n16:eng
n3:klicovaSlova
Stochastic programming, scenarios, worst case analysis, heuristic and genetic algorithms
n3:klicoveSlovo
n5:Stochastic%20programming n5:heuristic%20and%20genetic%20algorithms n5:scenarios n5:worst%20case%20analysis
n3:kontrolniKodProRIV
[BD00DA436C76]
n3:mistoVydani
Netherlands
n3:nazevEdiceCisloSvazku
1
n3:nazevZdroje
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.).
n3:obor
n10:BB
n3:pocetDomacichTvurcuVysledku
2
n3:pocetStranKnihy
588
n3:pocetTvurcuVysledku
2
n3:rokUplatneniVysledku
n4:2008
n3:tvurceVysledku
Roupec, Jan Popela, Pavel
n3:zamer
n19:MSM0021630529
s:numberOfPages
9
n20:hasPublisher
Springer-Verlag
n18:isbn
978-1-4020-8918-3
n8:organizacniJednotka
26210