The benefits of state aggregation with extreme-point weighting for assemble-to-order systems

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buir.contributor.authorNadar, Emre
dc.citation.epage1057en_US
dc.citation.issueNumber4en_US
dc.citation.spage1040en_US
dc.citation.volumeNumber66en_US
dc.contributor.authorNadar, Emreen_US
dc.contributor.authorAkçay, A.en_US
dc.contributor.authorAkan, M.en_US
dc.contributor.authorScheller Wolf, A.en_US
dc.date.accessioned2019-01-29T15:33:52Z
dc.date.available2019-01-29T15:33:52Z
dc.date.issued2018en_US
dc.departmentDepartment of Industrial Engineeringen_US
dc.description.abstractWe provide a new method for solving a very general model of an assemble-toorder system: multiple products, multiple components that may be demanded in different quantities by different products, batch production, random lead times, and lost sales, modeled as a Markov decision process under the discounted cost criterion. A control policy specifies when a batch of components should be produced and whether an arriving demand for each product should be satisfied. As optimal solutions for our model are computationally intractable for even moderately sized systems, we approximate the optimal cost function by reformulating it on an aggregate state space and restricting each aggregate state to be represented by its extreme original states. Our aggregation drastically reduces the value iteration computational burden. We derive an upper bound on the distance between aggregate and optimal solutions. This guarantees that the value iteration algorithm for the original problem initialized with the aggregate solution converges to the optimal solution. We also establish the optimality of a lattice-dependent base-stock and rationing policy in the aggregate problem when certain product and component characteristics are incorporated into the aggregation/disaggregation schemes. This enables us to further alleviate the value iteration computational burden in the aggregate problem by eliminating suboptimal actions. Leveraging all of our results, we can solve the aggregate problem for systems of up to 22 components, with an average distance of 11.09% from the optimal cost in systems of up to 4 components (for which we could solve the original problem to optimality).en_US
dc.description.provenanceSubmitted by Bilge Kat (bilgekat@bilkent.edu.tr) on 2019-01-29T15:33:52Z No. of bitstreams: 1 The_benefits_of_state_aggregation_with_extreme-point_weighting_for_assemble-to-order_systems.pdf: 1650729 bytes, checksum: d0c1c753f25a352aa0c6a3fe337e5d7c (MD5)en
dc.description.provenanceMade available in DSpace on 2019-01-29T15:33:52Z (GMT). No. of bitstreams: 1 The_benefits_of_state_aggregation_with_extreme-point_weighting_for_assemble-to-order_systems.pdf: 1650729 bytes, checksum: d0c1c753f25a352aa0c6a3fe337e5d7c (MD5) Previous issue date: 2018-07-24en
dc.identifier.doi10.1287/opre.2017.1710en_US
dc.identifier.eissn1526-5463
dc.identifier.issn0030-364X
dc.identifier.urihttp://hdl.handle.net/11693/48493
dc.language.isoEnglishen_US
dc.publisherInstitute for Operations Research and the Management Sciences (INFORMS)en_US
dc.relation.isversionofhttps://doi.org/10.1287/opre.2017.1710en_US
dc.source.titleOperations Researchen_US
dc.subjectAssemble-to-order systemsen_US
dc.subjectMarkov decision processesen_US
dc.subjectApproximate dynamic programmingen_US
dc.subjectAggregationen_US
dc.titleThe benefits of state aggregation with extreme-point weighting for assemble-to-order systemsen_US
dc.typeArticleen_US

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