Experimental Results Indicating Lattice-Dependent Policies May Be Optimal for General Assemble-To-Order Systems

Simple item page
dc.citation.epage661en_US
dc.citation.issueNumber4en_US
dc.citation.spage647en_US
dc.citation.volumeNumber25en_US
dc.contributor.authorNadar, E.en_US
dc.contributor.authorAkan, M.en_US
dc.contributor.authorScheller Wolf, A.en_US
dc.date.accessioned2018-04-12T11:12:37Z
dc.date.available2018-04-12T11:12:37Z
dc.date.issued2016en_US
dc.departmentDepartment of Industrial Engineeringen_US
dc.description.abstractWe consider an assemble-to-order (ATO) system with multiple products, multiple components which may be demanded in different quantities by different products, possible batch ordering of components, random lead times, and lost sales. We model the system as an infinite-horizon Markov decision process under the average 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. Previous work has shown that a lattice-dependent base-stock and lattice-dependent rationing (LBLR) policy is an optimal stationary policy for a special case of the ATO model presented here (the generalized M-system). In this study, we conduct numerical experiments to evaluate the use of an LBLR policy for our general ATO model as a heuristic, comparing it to two other heuristics from the literature: a state-dependent base-stock and state-dependent rationing (SBSR) policy, and a fixed base-stock and fixed rationing (FBFR) policy. Remarkably, LBLR yields the globally optimal cost in each of more than 22,500 instances of the general problem, outperforming SBSR and FBFR with respect to both objective value (by up to 2.6% and 4.8%, respectively) and computation time (by up to three orders and one order of magnitude, respectively) in 350 of these instances (those on which we compare the heuristics). LBLR and SBSR perform significantly better than FBFR when replenishment batch sizes imperfectly match the component requirements of the most valuable or most highly demanded product. In addition, LBLR substantially outperforms SBSR if it is crucial to hold a significant amount of inventory that must be rationed.en_US
dc.description.provenanceMade available in DSpace on 2018-04-12T11:12:37Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 179475 bytes, checksum: ea0bedeb05ac9ccfb983c327e155f0c2 (MD5) Previous issue date: 2016en
dc.identifier.doi10.1111/poms.12498en_US
dc.identifier.eissn1937-5956
dc.identifier.issn1059-1478
dc.identifier.urihttp://hdl.handle.net/11693/37408
dc.language.isoEnglishen_US
dc.publisherWiley-Blackwellen_US
dc.relation.isversionofhttp://dx.doi.org/10.1111/poms.12498en_US
dc.source.titleProduction and Operations Managementen_US
dc.subjectAssemble-To-Order systemsen_US
dc.subjectInventory managementen_US
dc.subjectLost salesen_US
dc.subjectMarkov decision processesen_US
dc.subjectMixed integer programen_US
dc.titleExperimental Results Indicating Lattice-Dependent Policies May Be Optimal for General Assemble-To-Order Systemsen_US
dc.typeArticleen_US

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Nadar_et_al-2016-Production_and_Operations_Management.pdf
Size:
326.48 KB
Format:
Adobe Portable Document Format
Description:
Full printable version
Download

Collections

Scholarly Publications - Industrial Engineering