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dc.contributor.advisorGökbayrak, Kağanen_US
dc.contributor.authorAvcı, Harunen_US
dc.date.accessioned2018-05-18T11:27:46Z
dc.date.available2018-05-18T11:27:46Z
dc.date.copyright2018-05
dc.date.issued2018-05
dc.date.submitted2018-05-17
dc.identifier.urihttp://hdl.handle.net/11693/46944
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 44-48).en_US
dc.description.abstractWe consider a discrete-time in nite-horizon inventory system with full backlogging, deterministic replenishment lead time, and Markov-modulated demand. The actual state of demand can only be imperfectly estimated based on past demand data. We model the inventory replenishment problem as a Markov decision process with an uncountable state space consisting of both the inventory position and the most recent belief about the actual state of demand. When the demand state evolves according to an ergodic Markov chain, using the vanishing discount method along with a coupling argument, we prove the existence of an optimal average cost that is independent of the initial system state. With this result, we establish the average-cost optimality of a belief-dependent base-stock policy. We then discretize the belief space into a regular grid. The average cost under our discretization converges to the optimal average cost as the number of grid points grows large. Finally, we conduct numerical experiments to evaluate the use of a myopic belief-dependent base-stock policy as a heuristic. On a test bed of 108 instances, the average cost under the myopic policy deviates by no more than a few percent from the best lower bound on the optimal average cost obtained from our discretization.en_US
dc.description.statementofresponsibilityby Harun Avcı.en_US
dc.format.extentviii, 48 leaves : charts ; 30 cmen_US
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectInventory Controlen_US
dc.subjectMarkov-Modulated Demanden_US
dc.subjectPartial Observationsen_US
dc.subjectLong-Run Average Costen_US
dc.subjectBase-Stock Policyen_US
dc.titleStructural results for average-cost inventory models with partially observed Markov-modulated demanden_US
dc.title.alternativeSaklı Markov süreciyle değişen talep dağılımlı ortalama maliyet envanter modellerinde yapısal sonuçlaren_US
dc.typeThesisen_US
dc.departmentDepartment of Industrial Engineeringen_US
dc.publisherBilkent Universityen_US
dc.description.degreeM.S.en_US
dc.identifier.itemidB158355
dc.embargo.release2019-01-01


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