Emily Lada is the SAS Simulation Studio Team Lead in the Operations Research department within the SAS Advanced Analytics Division. She is responsible for providing subject matter expertise to assist in the definition, design, and validation of required software features for SAS Simulation Studio. Emily earned a Ph.D. in Operations Research from North Carolina State University and she has worked for SAS Institute since 2005
In 1967, just a short time before a group of graduate students at North Carolina State University (NCSU) were starting a software company they called SAS, Dr. Salah Elmaghraby joined the faculty of NCSU as a Distinguished University Professor. In the 1970s, Dr. Elmaghraby led the effort to create one
In a previous post, I discussed using discrete-event simulation to validate an optimization model and its underlying assumptions. A similar approach can be used to validate queueing models as well. And when it is found that the assumptions required for a queueing model are not a good fit for the
The primary objective of many discrete-event simulation projects is system investigation. Output data from the simulation model are used to better understand the operation of the system (whether that system is real or theoretical), as well as to conduct various "what-if"-type analyses. However, I recently worked on another model
