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dc.contributor.advisor Westphal, Stephan Prof. Dr.
dc.contributor.author Tiedemann, Morten
dc.date.accessioned 2015-05-04T08:49:46Z
dc.date.available 2015-05-04T08:49:46Z
dc.date.issued 2015-05-04
dc.identifier.uri http://hdl.handle.net/11858/00-1735-0000-0022-5FCC-E
dc.language.iso eng de
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/
dc.subject.ddc 510 de
dc.title Online Resource Management de
dc.type doctoralThesis de
dc.contributor.referee Westphal, Stephan Prof. Dr.
dc.date.examination 2015-04-16
dc.description.abstracteng In this thesis, we consider several problems related to online resource management. In online optimization, an algorithm has to make decisions based on a sequence of incoming bits of information without knowledge of future inputs. We apply the well-established concept of competitive analysis in order to measure the quality of an online algorithm. First, we analyze an online knapsack problem with incremental capacity which extends the basic online knapsack problem by introducing a dynamic instead of a static knapsack capacity. This setting is applicable to classic problems such as resource allocation or one-way trading. Secondly, we expand the concept of competitive analysis to multi-objective online problems and achieve a novel and consistent framework for the analysis of multi-objective online problems. Finally, we present a real-world optimization problem, namely a cutting problem arising in the veneer industry, featuring uncertainty in the input data and solve this problem by means of deterministic and robust optimization. de
dc.contributor.coReferee Krumke, Sven O. Prof. Dr.
dc.subject.eng combinatorial optimization de
dc.subject.eng online optimization de
dc.subject.eng competitive analysis de
dc.subject.eng resource management de
dc.identifier.urn urn:nbn:de:gbv:7-11858/00-1735-0000-0022-5FCC-E-1
dc.affiliation.institute Fakultät für Mathematik und Informatik de
dc.subject.gokfull Mathematics (PPN61756535X) de
dc.identifier.ppn 823957764

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