From the reviews:
“The intended audience is graduate and advanced undergraduate students. … The book is clearly written, using a fairly terse style that may please instructors more than students. … its greatest strength is the attention it gives to the modeling process. … can be recommended as a supplement to any simulation course.” (Robert W. Hayden, MAA Reviews, March, 2014)
“The present book consists of 276 pages organised in 9 Chapters. … recommended for MSc, PhD students.” (János Sztrik, zbMATH, Vol. 1266, 2013)
This graduate-level text covers modeling, programming and analysis of simulation experiments and provides a rigorous treatment of the foundations of simulation and why it works. It introduces object-oriented programming for simulation, covers both the probabilistic and statistical basis for simulation in a rigorous but accessible manner (providing all necessary background material), and provides a modern treatment of experiment design and analysis that goes beyond classical statistics. The book emphasizes essential foundations throughout, rather than providing a compendium of algorithms and theorems, and prepares the reader to use simulation in research as well as practice.
The book is a rigorous but concise treatment, emphasizing lasting principles, but also providing specific training in modeling, programming and analysis. In addition to teaching readers how to do simulation, it also prepares them to use simulation in their research; no other book does this.
About the Author
Barry L. Nelson currently serves as the Chair of the Department of Industrial Engineering and Management Sciences at Northwestern, as well as past Editor in Chief of Naval Research Logistics. He has also received an award for the Charles Deering McCormick Professor of Teaching Excellence. Barry Nelson’s research is involved with the design and analysis of computer simulation experiments on models of stochastic systems, focusing particularly on issues related to statistical efficiency of simulation experiments. His research includes modeling and analysis of networks of nonstationary queues. The applications of this include computer-performance modeling, manufacturing systems, financial engineering and transportation.
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