A critical review of the approaches to optimization problems under uncertainty

Simple item page
buir.advisorTansel, Barbaros Ç.
dc.contributor.authorGürtuna, Filiz
dc.date.accessioned2016-01-08T18:02:58Z
dc.date.available2016-01-08T18:02:58Z
dc.date.issued2001
dc.descriptionAnkara : The Department of Industrial Engineering and the Institute of Engineering and Science of Bilkent University, 2001.en_US
dc.descriptionThesis (Master's) -- Bilkent University, 2001.en_US
dc.descriptionIncludes bibliographical references leaves 58-72.en_US
dc.description.abstractIn this study, the issue of uncertainty in optimization problems is studied. First of all, the meaning and sources of uncertainty are explained and then possible ways of its representation are analyzed. About the modelling process, different approaches as sensitivity analysis, parametric programming, robust optimization, stochastic programming, fuzzy programming, multiobjective programming and imprecise optimization are presented with advantages and disadvantages from different perspectives. Some extensions of the concepts of imprecise optimization are also presented.en_US
dc.description.provenanceMade available in DSpace on 2016-01-08T18:02:58Z (GMT). No. of bitstreams: 1 0001801.pdf: 479352 bytes, checksum: a6c137b8de24f4549bb31b829d30219a (MD5)en
dc.description.statementofresponsibilityGürtuna, Filizen_US
dc.format.extent72 leavesen_US
dc.identifier.urihttp://hdl.handle.net/11693/14608
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectuncertaintyen_US
dc.subjectimprecise optimizationen_US
dc.subjectmultiobjective optimizationen_US
dc.subjectfuzzy programmingen_US
dc.subjectstochastic programmingen_US
dc.subjectrobust optimizationen_US
dc.subjectparametric programmingen_US
dc.subjectsensitivity analysisen_US
dc.subject.lccQ375 .G87 2001en_US
dc.subject.lcshUncertainty (Information theory)en_US
dc.subject.lcshProgramming (Mathematics)en_US
dc.subject.lcshStochastic programming.en_US
dc.titleA critical review of the approaches to optimization problems under uncertaintyen_US
dc.typeThesisen_US
thesis.degree.disciplineIndustrial Engineering
thesis.degree.grantorBilkent University
thesis.degree.levelMaster's
thesis.degree.nameMS (Master of Science)

Files

Original bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
0001801.pdf
Size:
468.12 KB
Format:
Adobe Portable Document Format
Download

Collections

Graduate School of Engineering and Science