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Solution Methods for Multi-Objective Robust Combinatorial Optimization

dc.contributor.advisorSchöbel, Anita Prof. Dr.
dc.contributor.authorThom, Lisa
dc.date.accessioned2018-05-02T09:06:57Z
dc.date.available2018-05-02T09:06:57Z
dc.date.issued2018-05-02
dc.identifier.urihttp://hdl.handle.net/11858/00-1735-0000-002E-E3D4-D
dc.identifier.urihttp://dx.doi.org/10.53846/goediss-6852
dc.language.isoengde
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject.ddc510de
dc.titleSolution Methods for Multi-Objective Robust Combinatorial Optimizationde
dc.typedoctoralThesisde
dc.contributor.refereeSchöbel, Anita Prof. Dr.
dc.date.examination2018-04-19
dc.description.abstractengThis thesis addresses combinatorial optimization problems with several objectives containing uncertain parameters. A variety of robustness concepts for multi-objective optimization problems have been developed during the last years. This thesis provides methods to find so-called robust efficient solutions with respect to several of these concepts, assuming the uncertain parameters to be given via common uncertainty sets. Several solution approaches are presented, including extensions and combinations of algorithms from both robust and multi-objective optimization, using properties of particular uncertainty sets and robustness concepts. Beyond general combinatorial optimization problems, the shortest path problem is considered in particular. Solution algorithms are implemented and evaluated in numerical experiments.de
dc.contributor.coRefereeSchmidt, Marie Dr.
dc.subject.engMulti-objective robust optimizationde
dc.subject.engMulti-objective optimizationde
dc.subject.engRobust optimizationde
dc.subject.engCombinatorial optimizationde
dc.identifier.urnurn:nbn:de:gbv:7-11858/00-1735-0000-002E-E3D4-D-1
dc.affiliation.instituteFakultät für Mathematik und Informatikde
dc.subject.gokfullMathematik (PPN61756535X)de
dc.identifier.ppn1022271946


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