Production strategies for random yield processes.
dc.contributor.author | Grasman, Scott Erwin | |
dc.contributor.advisor | Birge, John R. | |
dc.contributor.advisor | Olsen, Tava L. | |
dc.date.accessioned | 2016-08-30T18:07:38Z | |
dc.date.available | 2016-08-30T18:07:38Z | |
dc.date.issued | 2000 | |
dc.identifier.uri | http://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.uri | https://hdl.handle.net/2027.42/132576 | |
dc.description.abstract | Classical 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.extent | 162 p. | |
dc.language | English | |
dc.language.iso | EN | |
dc.subject | Basestock | |
dc.subject | Polling | |
dc.subject | Production Strategies | |
dc.subject | Random Yield Processes | |
dc.title | Production strategies for random yield processes. | |
dc.type | Thesis | |
dc.description.thesisdegreename | PhD | en_US |
dc.description.thesisdegreediscipline | Applied Sciences | |
dc.description.thesisdegreediscipline | Industrial engineering | |
dc.description.thesisdegreediscipline | Operations research | |
dc.description.thesisdegreegrantor | University of Michigan, Horace H. Rackham School of Graduate Studies | |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/132576/2/9977163.pdf | |
dc.owningcollname | Dissertations and Theses (Ph.D. and Master's) |
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