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dc.contributor.author박철진-
dc.date.accessioned2017-10-16T05:20:31Z-
dc.date.available2017-10-16T05:20:31Z-
dc.date.issued2015-12-
dc.identifier.citationProceedings of the 2015 Winter Simulation Conference, Page. 484-492en_US
dc.identifier.isbn978-1-4673-9743-8-
dc.identifier.issn1558-4305-
dc.identifier.urihttp://ieeexplore.ieee.org/document/7408189/-
dc.identifier.urihttp://hdl.handle.net/20.500.11754/30042-
dc.description.abstractWe develop a graphical method, namely the bivariate measure of risk and error (BMORE) plot, to visualize bivariate output data from the stochastic simulation. The BMORE plot consists of a sample mean, median, minimum/maximum values for each measure, an outlier, and the boundary of a certain percentile of the simulation data on a two-dimensional space. In addition, it depicts confidence regions of both the true mean and the percentile to show how accurate the two estimates are. From the BMORE plot, scholars, practitioners, and software engineers in simulation fields can understand the variability and potential risk of the simulation data intuitively, design simulation experiments effectively, and reduce a great deal of time and effort to analyze the simulation results.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.subjectVisualizationen_US
dc.titleThe bivariate measure of risk and error (BMORE) ploten_US
dc.typeArticleen_US
dc.identifier.doi10.1109/WSC.2015.7408189-
dc.relation.page484-492-
dc.contributor.googleauthorLee, Mi Lim-
dc.contributor.googleauthorPark, Chuljin-
dc.sector.campusS-
dc.sector.daehakCOLLEGE OF ENGINEERING[S]-
dc.sector.departmentDEPARTMENT OF INDUSTRIAL ENGINEERING-
dc.identifier.pidparkcj-
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COLLEGE OF ENGINEERING[S](공과대학) > INDUSTRIAL ENGINEERING(산업공학과) > Articles
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