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Production strategies for random yield processes.

dc.contributor.authorGrasman, Scott Erwin
dc.contributor.advisorBirge, John R.
dc.contributor.advisorOlsen, Tava L.
dc.date.accessioned2016-08-30T18:07:38Z
dc.date.available2016-08-30T18:07:38Z
dc.date.issued2000
dc.identifier.urihttp://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqm&rft_dat=xri:pqdiss:9977163
dc.identifier.urihttps://hdl.handle.net/2027.42/132576
dc.description.abstractClassical inventory models provide optimal results for a wide variety of problems but do not apply directly to random yield systems. While random yield problems have been extensively studied, little attention has been given to multiple product systems. We present a queueing analysis of multiple product systems with setups and random yield and utilize these results to develop basestock production strategies for both backlogging and lost sales (or expediting). These results are related to bounds on the optimal production strategy. We present theoretical and applicable results for manufacturing environments and discuss other areas of application.
dc.format.extent162 p.
dc.languageEnglish
dc.language.isoEN
dc.subjectBasestock
dc.subjectPolling
dc.subjectProduction Strategies
dc.subjectRandom Yield Processes
dc.titleProduction strategies for random yield processes.
dc.typeThesis
dc.description.thesisdegreenamePhDen_US
dc.description.thesisdegreedisciplineApplied Sciences
dc.description.thesisdegreedisciplineIndustrial engineering
dc.description.thesisdegreedisciplineOperations research
dc.description.thesisdegreegrantorUniversity of Michigan, Horace H. Rackham School of Graduate Studies
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/132576/2/9977163.pdf
dc.owningcollnameDissertations and Theses (Ph.D. and Master's)


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