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dc.contributor.authorAkcay, A.en_US
dc.contributor.authorBiller, B.en_US
dc.date.accessioned2016-02-08T12:21:03Z
dc.date.available2016-02-08T12:21:03Z
dc.date.issued2015en_US
dc.identifier.issn0891-7736
dc.identifier.urihttp://hdl.handle.net/11693/28452
dc.description.abstractWe consider an assemble-to-order production system where the product demands and the time since the last customer arrival are not independent. The simulation of this system requires a multivariate input model that generates random input vectors with correlated discrete and continuous components. In this paper, we capture the dependence between input variables in an undirected graphical model and decouple the statistical estimation of the univariate input distributions and the underlying dependence measure into separate problems. The estimation errors due to finiteness of the real-world data introduce the so-called input uncertainty in the simulation output. We propose a method that accounts for input uncertainty by sampling the univariate empirical distribution functions via bootstrapping and by maintaining a posterior distribution of the precision matrix that corresponds to the dependence structure of the graphical model. The method improves the coverages of the confidence intervals for the expected profit the per period. © 2014 IEEE.en_US
dc.language.isoEnglishen_US
dc.source.titleProceedings - Winter Simulation Conferenceen_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/WSC.2014.7020057en_US
dc.subjectGraphic methodsen_US
dc.subjectConfidence intervalen_US
dc.subjectDependence measuresen_US
dc.subjectDependence structuresen_US
dc.subjectEmpirical distribution functionsen_US
dc.subjectInput distributionsen_US
dc.subjectPosterior distributionsen_US
dc.subjectSimulation outputsen_US
dc.subjectStatistical estimationen_US
dc.subjectDistribution functionsen_US
dc.titleQuantifying input uncertainty in an assemble-to-order system simulation with correlated input variables of mixed typesen_US
dc.typeConference Paperen_US
dc.departmentDepartment of Industrial Engineering
dc.citation.spage2124en_US
dc.citation.epage2135en_US
dc.citation.volumeNumber2015-Januaryen_US
dc.identifier.doi10.1109/WSC.2014.7020057en_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US


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