Show simple item record

2005-12-29Buch DOI: 10.18452/8348
A Comparative Study of Decomposition Algorithms for Stochastic Combinatorial Optimization
dc.contributor.authorNtaimo, Lewis
dc.contributor.authorSen, Suvrajeet
dc.contributor.editorHigle, Julie L.
dc.contributor.editorRömisch, Werner
dc.contributor.editorSen, Surrajeet
dc.date.accessioned2017-06-16T20:06:07Z
dc.date.available2017-06-16T20:06:07Z
dc.date.created2006-03-08
dc.date.issued2005-12-29
dc.date.submitted2005-08-11
dc.identifier.urihttp://edoc.hu-berlin.de/18452/9000
dc.description.abstractThis paper presents comparative computational results using three decomposition algorithms on a battery of instances drawn from three different applications. In order to preserve the commonalities among the algorithms in our experiments, we have designed a testbed which is used to study instances arising in server location under uncertainty, strategic supply chain planning under uncertainty, and stochastic bipartitite matching. Insights related to alternative implementation issues leading to more efficient implementations, benchmarks for serial processing, and scalability of the methods are also presented. The computational experience demonstrates the promising potential of the disjunctive decomposition $(D^2)$ approach towards solving several large-scale problem instances from the three application areas. Furthermore, the study shows that convergence of the $D^2$ methods for stochastic combinatorial optimization (SCO) is in fact attainable since the methods scale well with the number of scenarios.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.subjectStochastic mixed-integer programmingeng
dc.subjectdisjunctive decompositioneng
dc.subjectstochastic server locationeng
dc.subjectstrategic supply chain planningeng
dc.subjectstochastic matchingeng
dc.subject.ddc510 Mathematik
dc.titleA Comparative Study of Decomposition Algorithms for Stochastic Combinatorial Optimization
dc.typebook
dc.identifier.urnurn:nbn:de:kobv:11-10059954
dc.identifier.doihttp://dx.doi.org/10.18452/8348
local.edoc.pages31
local.edoc.type-nameBuch
local.edoc.container-typeseries
local.edoc.container-type-nameSchriftenreihe
dc.identifier.zdb2936317-2
bua.series.nameStochastic Programming E-Print Series
bua.series.issuenumber2005,19

Show simple item record