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dc.contributor.advisorKarsu, Özlem
dc.contributor.authorBashir, Bashir Abdullahi
dc.date.accessioned2018-09-13T07:32:04Z
dc.date.available2018-09-13T07:32:04Z
dc.date.copyright2018-09
dc.date.issued2018-09
dc.date.submitted2018-09-12
dc.identifier.urihttp://hdl.handle.net/11693/47860
dc.descriptionCataloged from PDF version of article.en_US
dc.descriptionThesis (M.S.): Bilkent University, Department of Industrial Engineering, İhsan Doğramacı Bilkent University, 2018.en_US
dc.descriptionIncludes bibliographical references (leaves 50-55).en_US
dc.description.abstractWe consider multi-objective optimization (MOP) problems where the decision maker (DM) has equity concerns. We assume that the preference model of the DM satisfies properties related to inequity-aversion, hence we focus on finding efficient solutions in line with the properties of inequity-averse preferences, namely the equitably efficient solutions. We discuss two algorithms for finding good subsets of equitably efficient solutions. In the first approach, we propose an algorithm that generates an evenly distributed subset of the set of equitably efficient solutions to be considered further by the DM. The second approach is an extension of an interactive approach developed for finding efficient solutions in the rational dominance sense and finds equitably efficient solutions in the preferred region of the DM. We illustrate these algorithms on equitable multi-objective knapsack problems that fund projects in di erent categories subject to a limited budget. We perform experiments to show and discuss the performances of the algorithms for three and five criteria settings. The experiments show that the first algorithm generates an evenly distributed subset in reasonable time, hence is advantageous in terms of solution time, compared to an approach that aims to find the whole set of equitably efficient solutions. The second approach is also shown to be a computationally efficient one that could be used in settings where the DM is willing to provide preference information.en_US
dc.description.statementofresponsibilityby Bashir Abdullahi Bashir.en_US
dc.format.extentix, 58 leaves : illustrations ; 30 cm.en_US
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMulti-Objective Knapsack Problemen_US
dc.subjectEquitable Preferencesen_US
dc.subjectEquitable Efficiencyen_US
dc.subjectGeneralized Lorenz Dominanceen_US
dc.titleGenerating evenly distributed equitably efficient solutions in multi-objective optimization problemsen_US
dc.title.alternativeÇok amaçlı optimizasyon problemlerinde eşit dağılımlı eşitlikçi verimli çözümler bulunmasıen_US
dc.typeThesisen_US
dc.departmentDepartment of Industrial Engineeringen_US
dc.publisherBilkent Universityen_US
dc.description.degreeM.S.en_US
dc.identifier.itemidB158950
dc.embargo.release2019-09-30


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