{"id":112880,"date":"2018-03-11T10:40:04","date_gmt":"2018-03-11T10:40:04","guid":{"rendered":"https:\/\/www.deberes.net\/tesis\/sin-categoria\/efficient-methodologies-for-the-treatment-of-large-scale-stochastic-optimization-problems\/"},"modified":"2018-03-11T10:40:04","modified_gmt":"2018-03-11T10:40:04","slug":"efficient-methodologies-for-the-treatment-of-large-scale-stochastic-optimization-problems","status":"publish","type":"post","link":"https:\/\/www.deberes.net\/tesis\/programacion-entera\/efficient-methodologies-for-the-treatment-of-large-scale-stochastic-optimization-problems\/","title":{"rendered":"Efficient methodologies for the treatment of large-scale stochastic optimization problems"},"content":{"rendered":"<h2>Tesis doctoral de <strong> Aitziber Unzueta Inchaurbe <\/strong><\/h2>\n<p>El \u00e1mbito de investigaci\u00f3n de este trabajo es la programaci\u00f3n estoc\u00e1stica, disciplina que trata de modelizar y resolver problemas de optimizaci\u00f3n bajo incertidumbre. En general, las aplicaciones reales son de grandes dimensiones, con la complicaci\u00f3n adicional de incluir variables 0-1. Ambas caracter\u00edsticas hacen que este tipo de problemas sean dif\u00edciles de resolver. En este trabajo se pretende abrir un camino en la exploraci\u00f3n de la obtenci\u00f3n de soluciones factibles cuasi \u00f3ptimas (en el peor de los casos)  para problemas mixtos 0-1 de grandes dimensiones.Se presenta la relajaci\u00f3n lagrangeana como metodolog\u00eda capaz de proporcionar una cota de la soluci\u00f3n \u00f3ptima. Dada la estructura del modelo determinista equivalente bietapa en formulaci\u00f3n extendida, se plantean dos descomposiciones. La descomposici\u00f3n que resulta al relajar las condiciones de noanticipatividad asociadas a los escenarios, y la resultante de relajar las condiciones de noanticipatividad asociadas a racimos de escenarios. A partir de la implementaci\u00f3n de distintos procedimientos computacionales en c++ junto con los solvers coin-or y cplex integrado en coin-or, se han llevado a cabo varias experiencias computacionales comparando el comportamiento de diferentes metodolog\u00edas para la actualizaci\u00f3n de los multiplicadores de lagrange como son: el m\u00e9todo del subgradiente, el algoritmo del volumen, el progressive hedging algorithm y el dynamic constrained cutting plane method; as\u00ed como las dos descomposiciones propuestas y los dos solvers utilizados.Finalmente, debido a los buenos resultados obtenidos en dos etapas, se propone la extensi\u00f3n de dicha metodolog\u00eda a problemas estoc\u00e1sticos multietapa mixtos 0-1.<\/p>\n<p>&nbsp;<\/p>\n<h3>Datos acad\u00e9micos de la tesis doctoral \u00ab<strong>Efficient methodologies for the treatment of large-scale stochastic optimization problems<\/strong>\u00ab<\/h3>\n<ul>\n<li><strong>T\u00edtulo de la tesis:<\/strong>\u00a0 Efficient methodologies for the treatment of large-scale stochastic optimization problems <\/li>\n<li><strong>Autor:<\/strong>\u00a0 Aitziber Unzueta Inchaurbe <\/li>\n<li><strong>Universidad:<\/strong>\u00a0 Pa\u00eds vasco\/euskal herriko unibertsitatea<\/li>\n<li><strong>Fecha de lectura de la tesis:<\/strong>\u00a0 26\/06\/2012<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h3>Direcci\u00f3n y tribunal<\/h3>\n<ul>\n<li><strong>Director de la tesis<\/strong>\n<ul>\n<li>Mar\u00eda Araceli Gar\u00edn Mart\u00edn<\/li>\n<\/ul>\n<\/li>\n<li><strong>Tribunal<\/strong>\n<ul>\n<li>Presidente del tribunal: laureano fernando Escudero bueno <\/li>\n<li>Mar\u00eda  teresa Vespucci &#8212; (vocal)<\/li>\n<li>Juan  Francisco Monge ivars (vocal)<\/li>\n<li>Mar\u00eda teresa Ortu\u00f1o s\u00e1nchez (vocal)<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Tesis doctoral de Aitziber Unzueta Inchaurbe El \u00e1mbito de investigaci\u00f3n de este trabajo es la programaci\u00f3n estoc\u00e1stica, disciplina que trata [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""}},"footnotes":""},"categories":[16880,16191,12909,11392],"tags":[224395,157059,11396,190559,190558,224396],"class_list":["post-112880","post","type-post","status-publish","format-standard","hentry","category-construccion-de-algoritmos","category-metodos-iterativos","category-pais-vasco-euskal-herriko-unibertsitatea","category-programacion-entera","tag-aitziber-unzueta-inchaurbe","tag-juan-francisco-monge-ivars","tag-laureano-fernando-escudero-bueno","tag-maria-araceli-garin-martin","tag-maria-teresa-ortuno-sanchez","tag-maria-teresa-vespucci"],"_links":{"self":[{"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/posts\/112880","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/comments?post=112880"}],"version-history":[{"count":0,"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/posts\/112880\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/media?parent=112880"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/categories?post=112880"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/tags?post=112880"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}