Tractable supply chain production planning, modeling nonlinear lead time and quality of service constraints

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dc.authorid0000-0002-3094-3718
dc.authorid0000-0001-6224-1166
dc.contributor.authorAnlı, Osman Muraten_US
dc.contributor.authorCaramanis, Michael C.en_US
dc.contributor.authorPaschalidis, Ioannis Ch.en_US
dc.date.accessioned2015-01-15T23:00:51Z
dc.date.available2015-01-15T23:00:51Z
dc.date.issued2007
dc.departmentIşık Üniversitesi, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümüen_US
dc.departmentIşık University, Faculty of Engineering, Department of Industrial Engineeringen_US
dc.descriptionNSF Grant DMI-0300359 is acknowledged for partial support of the research reported here.en_US
dc.description.abstractThis paper addresses the task of coordinated planning of a supply chain (SC). Work in process (WIP) in each facility participating in the SC, finished goods inventory, and backlogged demand costs are minimized over the planning horizon. In addition to the usual modeling of linear material flow balance equations, variable lead time (LT) requirements, resulting from the increasing incremental WIP as a facility's utilization increases, are also modeled. In recognition of the emerging significance of quality of service (QoS), that is control of stockout probability to meet demand on time, maximum stockout probability constraints are also modeled explicitly. Lead time and QoS modeling require incorporation of nonlinear constraints in the production planning optimization process. The quantification of these nonlinear constraints must capture statistics of the stochastic behaviour of production facilities revealed during a time scale for shorter than the customary weekly time scale of the planning process. The apparent computational complexity of planning production against variable LT and QoS constraints has long resulted in MRP-based scheduling practices that ignore the LT and QoS constraints has long resulted in MRP-based scheduling practices that ignore the LT and QoS impact to the plan's detriment. The computational complexity challenge was overcome by proposing and adopting a time-scale decomposition approach to production planning where short-time-scale stochastic dynamics are modeled in multiple facility-specific subproblems that receive tentative targets from a deterministic master problem and return statistics to it. A converging and scalable iterative methodology is implemented, providing evidence that significantly lower cost production plans are achievable in a computationally tractable manner.en_US
dc.description.sponsorshipNational Science Foundationen_US
dc.description.versionPublisher's Versionen_US
dc.identifier.citationAnli, O. M., Caramanis, M. C. & Paschalidis, I. C. (2007). Tractable supply chain production planning, modeling nonlinear lead time and quality of service constraints. Journal of Manufacturing Systems, 26(2), 116-134. doi:10.1016/j.jmsy.2008.05.001en_US
dc.identifier.doi10.1016/j.jmsy.2008.05.001
dc.identifier.endpage134
dc.identifier.issn0278-6125
dc.identifier.issn1878-6642
dc.identifier.issue2
dc.identifier.scopus2-s2.0-48149106876
dc.identifier.scopusqualityQ1
dc.identifier.startpage116
dc.identifier.urihttps://hdl.handle.net/11729/273
dc.identifier.urihttp://dx.doi.org/10.1016/j.jmsy.2008.05.001
dc.identifier.volume26
dc.identifier.wosWOS:000261706700007
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakScience Citation Index Expanded (SCI-EXPANDED)en_US
dc.institutionauthorAnlı, Osman Muraten_US
dc.institutionauthorid0000-0002-3094-3718
dc.language.isoenen_US
dc.peerreviewedYesen_US
dc.publicationstatusPublisheden_US
dc.publisherElsevier Ltden_US
dc.relation.ispartofJournal of Manufacturing Systemsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectManufacturing flow controllersen_US
dc.subjectPerturbation analysisen_US
dc.subjectProduction systemsen_US
dc.subjectInventory controlen_US
dc.subjectLarge deviationsen_US
dc.subjectNetwork modelen_US
dc.subjectFluid networken_US
dc.subjectJob-shopen_US
dc.subjectPoliciesen_US
dc.subjectDesignen_US
dc.subjectComputational complexityen_US
dc.subjectLeaden_US
dc.subjectLinear equationsen_US
dc.subjectManufactureen_US
dc.subjectMathematical techniquesen_US
dc.subjectPlanningen_US
dc.subjectProbabilityen_US
dc.subjectProcess engineeringen_US
dc.subjectProcess planningen_US
dc.subjectProduction controlen_US
dc.subjectProduction engineeringen_US
dc.subjectProject managementen_US
dc.subjectQuality controlen_US
dc.subjectStatistical methodsen_US
dc.subjectStochastic programmingen_US
dc.subjectSupply chain managementen_US
dc.subjectSupply chainsen_US
dc.subjectCoordinated planningen_US
dc.subjectFinished goods (FG)en_US
dc.subjectLead timeen_US
dc.subjectLower costen_US
dc.subjectManufacturing engineersen_US
dc.subjectMaterial flowsen_US
dc.subjectNon linear constrainten_US
dc.subjectOn timeen_US
dc.subjectOptimization processesen_US
dc.subjectPaper addressesen_US
dc.subjectPlanning horizonsen_US
dc.subjectPlanning process (CPPS)en_US
dc.subjectProbability constraintsen_US
dc.subjectProduction facilitiesen_US
dc.subjectProduction planningen_US
dc.subjectQoS constraintsen_US
dc.subjectQuality of service (QoS)en_US
dc.subjectQuality of Service (QoS) constraintsen_US
dc.subjectStochastic behavioren_US
dc.subjectStochastic dynamicsen_US
dc.subjectStock outsen_US
dc.subjectSub-problemsen_US
dc.subjectSupply chain (SC)en_US
dc.subjectTime scale decompositionen_US
dc.subjectTime scalingen_US
dc.subjectVariable lead timeen_US
dc.subjectWork-in-process (WIP)en_US
dc.subjectQuality of serviceen_US
dc.titleTractable supply chain production planning, modeling nonlinear lead time and quality of service constraintsen_US
dc.typeArticleen_US
dspace.entity.typePublication

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