About: Genetic Algorithms for Scenario Generation in Stochastic Programming: Motivation and General Framework

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rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
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 (en)
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 (en)
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 (en)
skos:notation	RIV/00216305:26210/08:PU77910!RIV12-MSM-26210___
http://linked.open...avai/riv/aktivita	Z
http://linked.open...avai/riv/aktivity	Z(MSM0021630529)
http://linked.open...vai/riv/dodaniDat	2012
http://linked.open...aciTvurceVysledku	Popela, Pavel Roupec, Jan
http://linked.open.../riv/druhVysledku	C - Kapitola v knize
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	Vysoké učení technické v Brně / Fakulta strojního inženýrství
http://linked.open...dnocenehoVysledku	369097
http://linked.open...ai/riv/idVysledku	RIV/00216305:26210/08:PU77910
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	Stochastic programming, scenarios, worst case analysis, heuristic and genetic algorithms (en)
http://linked.open.../riv/klicoveSlovo	scenarios Stochastic programming heuristic and genetic algorithms worst case analysis
http://linked.open...ontrolniKodProRIV	[BD00DA436C76]
http://linked.open...i/riv/mistoVydani	Netherlands
http://linked.open...vEdiceCisloSvazku	1
http://linked.open...i/riv/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.).
http://linked.open...in/vavai/riv/obor	BB
http://linked.open...ichTvurcuVysledku	2 (xsd:int)
http://linked.open...v/pocetStranKnihy	588 (xsd:int)
http://linked.open...cetTvurcuVysledku	2 (xsd:int)
http://linked.open...UplatneniVysledku	2008
http://linked.open...iv/tvurceVysledku	Popela, Pavel Roupec, Jan
http://linked.open...n/vavai/riv/zamer	Inteligentní systémy v automatizaci
number of pages	9 (xsd:int)
http://purl.org/ne...btex#hasPublisher	Springer-Verlag
https://schema.org/isbn	978-1-4020-8918-3
http://localhost/t...ganizacniJednotka	26210

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