Comparing Gradient Estimation Methods Applied to Stochastic Manufacturing Systems

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dc.contributor.authorMellacheruvu, Praveen V.en_US
dc.contributor.authorFu, Michael C.en_US
dc.contributor.authorHerrmann, Jeffrey W.en_US
dc.contributor.departmentISRen_US
dc.date.accessioned2007-05-23T10:09:10Z
dc.date.available2007-05-23T10:09:10Z
dc.date.issued2000en_US
dc.description.abstractThis paper compares two gradient estimation methods that can be usedfor estimating the sensitivities of output metrics with respectto the input parameters of a stochastic manufacturing system.A brief description of the methods used currently is followedby a description of the two methods: the finite difference methodand the simultaneous perturbation method. While the finitedifference method has been in use for a long time, simultaneousperturbation is a relatively new method which has beenapplied with stochastic approximation for optimizationwith good results. The methods described are used to analyzea stochastic manufacturing system and estimate gradients.The results are compared to the gradients calculated fromanalytical queueing system models.<p>These gradient methods are of significant use in complex manufacturingsystems like semiconductor manufacturing systems where we havea large number of input parameters which affect the average total cycle time.These gradient estimation methods can estimate the impact thatthese input parameters have and identify theparameters that have the maximum impact on system performance.en_US
dc.format.extent227583 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/1903/6123
dc.language.isoen_USen_US
dc.relation.ispartofseriesISR; TR 2000-1en_US
dc.subjectalgorithmsen_US
dc.subjectdiscrete event dynamical systems DEDSen_US
dc.subjectmanufacturingen_US
dc.subjectsensitivity analysisen_US
dc.subjectmanufacturing systemsen_US
dc.subjectSystems Integration Methodologyen_US
dc.titleComparing Gradient Estimation Methods Applied to Stochastic Manufacturing Systemsen_US
dc.typeTechnical Reporten_US

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