Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 박철진 | - |
dc.date.accessioned | 2017-10-16T05:20:31Z | - |
dc.date.available | 2017-10-16T05:20:31Z | - |
dc.date.issued | 2015-12 | - |
dc.identifier.citation | Proceedings of the 2015 Winter Simulation Conference, Page. 484-492 | en_US |
dc.identifier.isbn | 978-1-4673-9743-8 | - |
dc.identifier.issn | 1558-4305 | - |
dc.identifier.uri | http://ieeexplore.ieee.org/document/7408189/ | - |
dc.identifier.uri | http://hdl.handle.net/20.500.11754/30042 | - |
dc.description.abstract | We 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.iso | en | en_US |
dc.publisher | IEEE | en_US |
dc.subject | Visualization | en_US |
dc.title | The bivariate measure of risk and error (BMORE) plot | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1109/WSC.2015.7408189 | - |
dc.relation.page | 484-492 | - |
dc.contributor.googleauthor | Lee, Mi Lim | - |
dc.contributor.googleauthor | Park, Chuljin | - |
dc.sector.campus | S | - |
dc.sector.daehak | COLLEGE OF ENGINEERING[S] | - |
dc.sector.department | DEPARTMENT OF INDUSTRIAL ENGINEERING | - |
dc.identifier.pid | parkcj | - |
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