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Random Demand Satisfaction in Unreliable Production–Inventory–Customer Systems

dc.contributor.authorLi, Jingshanen_US
dc.contributor.authorEnginarlar, Emreen_US
dc.contributor.authorMeerkov, Semyon M.en_US
dc.date.accessioned2006-09-11T14:18:17Z
dc.date.available2006-09-11T14:18:17Z
dc.date.issued2004-02en_US
dc.identifier.citationLi, Jingshan; Enginarlar, Emre; Meerkov, Semyon M.; (2004). "Random Demand Satisfaction in Unreliable Production–Inventory–Customer Systems." Annals of Operations Research 126 (1-4): 159-175. <http://hdl.handle.net/2027.42/44112>en_US
dc.identifier.issn0254-5330en_US
dc.identifier.issn1572-9338en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/44112
dc.description.abstractA method for calculating the probability of customer demand satisfaction in production–inventory–customer systems with Markovian machines, finite finished goods buffers, and random demand is developed. Using this method, the degradation of this probability as a function of demand variability is quantified. In addition, it is shown by examples that the probability of customer demand satisfaction depends primarily on the coefficient of variation, rather than on the complete distribution, of the demand.en_US
dc.format.extent169298 bytes
dc.format.extent3115 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.language.isoen_US
dc.publisherKluwer Academic Publishers; Springer Science+Business Mediaen_US
dc.subject.otherEconomics / Management Scienceen_US
dc.subject.otherTheory of Computationen_US
dc.subject.otherCombinatoricsen_US
dc.subject.otherOperation Research/Decision Theoryen_US
dc.subject.otherDue-time Performanceen_US
dc.subject.otherFinished Goods Bufferen_US
dc.subject.otherRandom Demanden_US
dc.subject.otherMarkovian Reliabilityen_US
dc.titleRandom Demand Satisfaction in Unreliable Production–Inventory–Customer Systemsen_US
dc.typeArticleen_US
dc.subject.hlbsecondlevelIndustrial and Operations Engineeringen_US
dc.subject.hlbsecondlevelManagementen_US
dc.subject.hlbsecondlevelEconomicsen_US
dc.subject.hlbtoplevelEngineeringen_US
dc.subject.hlbtoplevelBusinessen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumDepartment of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, 48109-2122, USAen_US
dc.contributor.affiliationumDepartment of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, 48109-2122, USAen_US
dc.contributor.affiliationumDepartment of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, 48109-2122, USAen_US
dc.contributor.affiliationumcampusAnn Arboren_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/44112/1/10479_2004_Article_5254653.pdfen_US
dc.identifier.doihttp://dx.doi.org/10.1023/B:ANOR.0000012279.78938.6ben_US
dc.identifier.sourceAnnals of Operations Researchen_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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