Sometimes, a system is too complex for a "closed-form" mathematical solution. This is where simulation comes in. Stewart provides a bridge between pure math and practical software execution, detailing how to generate random variables and analyze the statistical significance of simulation results. Why the 2009 Hardcover Edition Remains a Staple
With Markov chains as a tool, Stewart systematically dismantles the canonical queueing models (Kendall’s notation: A/S/c/K/N/D). The progression is logical:
No text is perfect. A few reviewers note that Stewart’s simulation coverage, while mathematically correct, lacks modern programming examples (e.g., no R or Python code). Furthermore, the section on Markov decision processes (MDPs) is brief. Readers interested in control will need supplementary texts. However, for performance modeling —predicting, not controlling—the coverage is exhaustive.
As a reference text that users return to for years, the hardcover binding is a practical choice for a book that often exceeds 700 pages of dense, vital information. Practical Applications Today
