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Abstract
Simulation models of real-life systems often assume stationary (homogeneous) Poisson arrivals. Therefore, when nonstationary arrival processes are required it is natural to assume Poisson arrivals with a time-varying arrival rate. For many systems, however, this provides an inaccurate representation of the arrival process which is either more or less variable than Poisson, and may exhibit dependence. We extend techniques that transform a stationary Poisson arrival process into a nonstationary Poisson arrival process by transforming a stationary arrival process into a nonstationary, non-Poisson (NSNP) arrival process. We show that the desired arrival rate is achieved, and that certain variability and dependence properties of the base process re
passed on to the transformed process. We also provide techniques for specifying the base process when presented with characteristics of, or data from, an arrival process and illustrate them by modeling e-mail arrival data.
(Joint work with Ira Gerhardt, Manhattan College)
Bio
Dr. Barry Nelson is well-known for his contributions to the design and analysis of computer simulation experiments on models of stochastic systems, particularly statistical efficiency, optimization via simulation, and multivariate input modeling and metamodeling. His research is driven by applications from areas such as computer-performance modeling, manufacturing systems, financial engineering and transportation.
Barry received his PhD in 1983 from Purdue University, and joined the faculty of Northwestern University after a decade at The Ohio State University. He currently serves as the Chair of the Department of Industrial Engineering and Management Sciences at Northwestern University. Among his awards and achievements, he has been chosen as an INFORMS fellow and received numerous prestigious best-paper as well as teaching awards.
