A new decomposition algorithm for multistage stochastic programs with endogenous uncertainties
Identifieur interne : 005957 ( Main/Exploration ); précédent : 005956; suivant : 005958A new decomposition algorithm for multistage stochastic programs with endogenous uncertainties
Auteurs : Vijay Gupta [États-Unis] ; Ignacio E. Grossmann [États-Unis]Source :
- Computers & chemical engineering [ 0098-1354 ] ; 2014.
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- Pascal (Inist)
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- topic : Norme, Industrie pétrolière.
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Abstract
In this paper, we present a new decomposition algorithm for solving large-scale multistage stochastic programs (MSSPs) with endogenous uncertainties. Instead of dualizing all the initial non-anticipativity constraints (NACs) and removing all the conditional NACs to decompose the problem into scenario subproblems, the basic idea relies on keeping a subset of NACs as explicit constraints in the scenario group subproblems while dualizing or relaxing the rest of the NACs. It is proved that the algorithm provides a dual bound that is at least as tight as the standard approach. Numerical results for process network examples and oilfield development planning problem are presented to illustrate that the proposed decomposition approach yields significant improvement in the dual bound at the root node and reduction in the total computational expense for closing the gap.
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Grossmann</name><affiliation wicri:level="4"><inist:fA14 i1="01"><s1>Department of Chemical Engineering, Carnegie Mellon University</s1><s2>Pittsburgh, PA 15213</s2><s3>USA</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ></inist:fA14><country>États-Unis</country><placeName><settlement type="city">Pittsburgh</settlement><region type="state">Pennsylvanie</region></placeName><orgName type="university">Université Carnegie-Mellon</orgName></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">INIST</idno><idno type="inist">14-0117520</idno><date when="2014">2014</date><idno type="stanalyst">PASCAL 14-0117520 INIST</idno><idno type="RBID">Pascal:14-0117520</idno><idno type="wicri:Area/PascalFrancis/Corpus">000B96</idno><idno type="wicri:Area/PascalFrancis/Curation">003A26</idno><idno type="wicri:Area/PascalFrancis/Checkpoint">000D67</idno><idno type="wicri:explorRef" wicri:stream="PascalFrancis" wicri:step="Checkpoint">000D67</idno><idno type="wicri:doubleKey">0098-1354:2014:Gupta V:a:new:decomposition</idno><idno type="wicri:Area/Main/Merge">005C90</idno><idno type="wicri:Area/Main/Curation">005957</idno><idno type="wicri:Area/Main/Exploration">005957</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a">A new decomposition algorithm for multistage stochastic programs with endogenous uncertainties</title><author><name sortKey="Gupta, Vijay" sort="Gupta, Vijay" uniqKey="Gupta V" first="Vijay" last="Gupta">Vijay Gupta</name><affiliation wicri:level="4"><inist:fA14 i1="01"><s1>Department of Chemical Engineering, Carnegie Mellon University</s1><s2>Pittsburgh, PA 15213</s2><s3>USA</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ></inist:fA14><country>États-Unis</country><placeName><settlement type="city">Pittsburgh</settlement><region type="state">Pennsylvanie</region></placeName><orgName type="university">Université Carnegie-Mellon</orgName></affiliation></author><author><name sortKey="Grossmann, Ignacio E" sort="Grossmann, Ignacio E" uniqKey="Grossmann I" first="Ignacio E." last="Grossmann">Ignacio E. Grossmann</name><affiliation wicri:level="4"><inist:fA14 i1="01"><s1>Department of Chemical Engineering, Carnegie Mellon University</s1><s2>Pittsburgh, PA 15213</s2><s3>USA</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ></inist:fA14><country>États-Unis</country><placeName><settlement type="city">Pittsburgh</settlement><region type="state">Pennsylvanie</region></placeName><orgName type="university">Université Carnegie-Mellon</orgName></affiliation></author></analytic><series><title level="j" type="main">Computers & chemical engineering</title><title level="j" type="abbreviated">Comput. chem. eng.</title><idno type="ISSN">0098-1354</idno><imprint><date when="2014">2014</date></imprint></series></biblStruct></sourceDesc><seriesStmt><title level="j" type="main">Computers & chemical engineering</title><title level="j" type="abbreviated">Comput. chem. eng.</title><idno type="ISSN">0098-1354</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Constraint satisfaction</term><term>Decomposition method</term><term>Endogenous</term><term>Geophysical prospecting</term><term>Large scale</term><term>Multistage apparatus</term><term>Oil industry</term><term>Problem solving</term><term>Risk factor</term><term>Root</term><term>Script</term><term>Standards</term><term>Stochastic Décomposition</term><term>Stochastic programming</term><term>Uncertain system</term></keywords><keywords scheme="Pascal" xml:lang="fr"><term>Décomposition stochastique</term><term>Endogène</term><term>Système incertain</term><term>Facteur risque</term><term>Echelle grande</term><term>Satisfaction contrainte</term><term>Scénario</term><term>Norme</term><term>Appareil étagé</term><term>Industrie pétrolière</term><term>Racine</term><term>Prospection géophysique</term><term>Programmation stochastique</term><term>Méthode décomposition</term><term>Résolution problème</term><term>.</term></keywords><keywords scheme="Wicri" type="topic" xml:lang="fr"><term>Norme</term><term>Industrie pétrolière</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">In this paper, we present a new decomposition algorithm for solving large-scale multistage stochastic programs (MSSPs) with endogenous uncertainties. Instead of dualizing all the initial non-anticipativity constraints (NACs) and removing all the conditional NACs to decompose the problem into scenario subproblems, the basic idea relies on keeping a subset of NACs as explicit constraints in the scenario group subproblems while dualizing or relaxing the rest of the NACs. It is proved that the algorithm provides a dual bound that is at least as tight as the standard approach. Numerical results for process network examples and oilfield development planning problem are presented to illustrate that the proposed decomposition approach yields significant improvement in the dual bound at the root node and reduction in the total computational expense for closing the gap.</div></front></TEI><affiliations><list><country><li>États-Unis</li></country><region><li>Pennsylvanie</li></region><settlement><li>Pittsburgh</li></settlement><orgName><li>Université Carnegie-Mellon</li></orgName></list><tree><country name="États-Unis"><region name="Pennsylvanie"><name sortKey="Gupta, Vijay" sort="Gupta, Vijay" uniqKey="Gupta V" first="Vijay" last="Gupta">Vijay Gupta</name></region><name sortKey="Grossmann, Ignacio E" sort="Grossmann, Ignacio E" uniqKey="Grossmann I" first="Ignacio E." last="Grossmann">Ignacio E. Grossmann</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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