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2004-05-17Buch DOI: 10.18452/8319
Assessing policy quality in multi-stage stochastic programming
dc.contributor.authorChiralaksanakul, Anukal
dc.contributor.authorMorton, David P.
dc.contributor.editorHigle, Julie L.
dc.contributor.editorRömisch, Werner
dc.contributor.editorSen, Surrajeet
dc.date.accessioned2017-06-16T19:58:48Z
dc.date.available2017-06-16T19:58:48Z
dc.date.created2006-03-02
dc.date.issued2004-05-17
dc.date.submitted2004-01-26
dc.identifier.urihttp://edoc.hu-berlin.de/18452/8971
dc.description.abstractSolving a multi-stage stochastic program with a large number of scenarios and a moderate-to-large number of stages can be computationally challenging. We develop two Monte Carlo-based methods that exploit special structures to generate feasible policies. To establish the quality of a given policy, we employ a Monte Carlo-based lower bound (for minimization problems) and use it to construct a confidence interval on the policy's optimality gap. The confidence interval can be formed in a number of ways depending on how the expected solution value of the policy is estimated and combined with the lower-bound estimator. Computational results suggest that a confidence interval formed by a tree-based gap estimator may be an effective method for assessing policy quality. Variance reduction is achieved by using common random numbers in the gap estimator.eng
dc.language.isoeng
dc.publisherHumboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, Institut für Mathematik
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subject.ddc510 Mathematik
dc.titleAssessing policy quality in multi-stage stochastic programming
dc.typebook
dc.identifier.urnurn:nbn:de:kobv:11-10059513
dc.identifier.doihttp://dx.doi.org/10.18452/8319
local.edoc.container-titleStochastic Programming E-Print Series
local.edoc.pages36
local.edoc.type-nameBuch
local.edoc.container-typeseries
local.edoc.container-type-nameSchriftenreihe
local.edoc.container-volume2004
local.edoc.container-issue12
local.edoc.container-erstkatid2936317-2

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