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dc.contributor.authorBiller, B.en_US
dc.contributor.authorAkçay, Alpen_US
dc.contributor.authorÇorlu, C.en_US
dc.contributor.authorTayur, S.en_US
dc.coverage.spatialSavanah, GA, USAen_US
dc.date.accessioned2016-02-08T12:14:30Z
dc.date.available2016-02-08T12:14:30Z
dc.date.issued2014en_US
dc.identifier.issn0891-7736en_US
dc.identifier.urihttp://hdl.handle.net/11693/28219
dc.descriptionDate of Conference: 7-10 December 2014en_US
dc.descriptionConference Name: 2014 Winter Simulation Conference, WSC 2014en_US
dc.description.abstractDependencies occur naturally between input processes of many manufacturing and service applications. When the dependence parameters are known with certainty, the failure to factor the dependencies into decisions is well known to waste significant resources in system management. Our focus is on the case of unknown dependence parameters that must be estimated from finite amounts of historical input data. In this case, the estimates of the unknown dependence parameters are random variables and simulations are designed to account for the dependence parameter uncertainty to better support the data-driven decision making. The premise of our paper is that there are certain cases in which the assumption of an independent input process to minimize the expected cost of input parameter uncertainty becomes preferable to accounting for the dependence parameter uncertainty in the simulation. Therefore, a fundamental question to answer before capturing the dependence parameter uncertainty in a stochastic system simulation is whether there is sufficient statistical evidence to represent the dependence, despite the uncertainty around its estimate, in the presence of limited data. We seek an answer for this question within a data-driven inventory-management context by considering an intermittent demand process with correlated demand size and number of interdemand periods. We propose two new finite-sample hypothesis tests to serve as the decision support tools determining when to ignore the correlation and when to account for the correlation together with the uncertainty around its estimate. We show that a statistical test accounting for the expected cost of correlation parameter uncertainty tends to reject the independence assumption less frequently than a statistical test which only considers the sampling distribution of the correlation-parameter estimator. The use of these tests is illustrated with examples and insights are provided into operational testing for dependence modeling.en_US
dc.language.isoEnglishen_US
dc.source.titleProceedings of the 2014 Winter Simulation Conference, WSC 2014en_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/WSC.2014.7019950en_US
dc.subjectCost accountingen_US
dc.subjectDecision makingen_US
dc.subjectDecision support systemsen_US
dc.subjectInventory controlen_US
dc.subjectParameter estimationen_US
dc.subjectStatistical testsen_US
dc.subjectStochastic systemsen_US
dc.subjectWaste managementen_US
dc.subjectCorrelation parametersen_US
dc.subjectData driven decisionen_US
dc.subjectDecision support toolsen_US
dc.subjectIndependence assumptionen_US
dc.subjectParameter uncertaintyen_US
dc.subjectSampling distributionen_US
dc.subjectService applicationsen_US
dc.subjectStatistical evidenceen_US
dc.subjectUncertainty analysisen_US
dc.titleA simulation-based support tool for data-driven decision making: operational testing for dependence modelingen_US
dc.typeConference Paperen_US
dc.departmentDepartment of Industrial Engineeringen_US
dc.citation.spage899en_US
dc.citation.epage909en_US
dc.identifier.doi10.1109/WSC.2014.7019950en_US
dc.publisherIEEEen_US


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