• Chapter 1 on basic simulation modeling now uses Python for all models.
• The NEW Chapter 2 on modeling and analysis of complex systems presents detailed analyses of supply chain, manufacturing, and communications network simulation models.
• Chapter 3 on simulation software discusses what are arguably the three leading and most-innovative simulation products.  A common example is given for these products.
• Chapter 4 now shows the utter danger of applying classical statistical methods that assume independence to the analysis of the output data from a single simulation run.
• Chapter 5 on model validation discusses in detail the 12 most-important techniques for building valid/credible simulation models.  It also shows the “proper” documentation for a simulation model corresponding to a real study.
• For Chapter 6 on selecting input probability distributions, a Student version of the ExpertFit distribution-fitting software is available on the book’s website for analyzing the data sets corresponding to the examples and problems.
• Chapter 7 on random-number generators discusses the best algorithms that are currently available, as well as important features such as good statistical performance, a large period, and multiple, disjoint random-number streams.
• Chapter 9 on output-data analysis has been simplified to present statistical techniques that are easy to understand, easy to implement, and highly-effective for all major problems of interest.
• Chapter 10 on comparing alternative system configurations now shows how to make the comparison based on the probability of a certain event’s occuring, which is often of interest in real-world applications.
• Chapter 12 on experimental design and optimization now focusses on designs and analyses that are the most applicable to simulation rather than physical experiments. Neural networks are introduced as a highly-effective methodology for developing metamodels (surrogate models).
• Chapter 13 on agent-based simulation and system dynamics argues that an agent-based model is not a fundamentally new type of simulation, but rather an alternative approach for performing discrete-event simulation, with alternatives being the process and event-scheduling approaches.
• The NEW Chapter 14 shows how AI/machine learning can be used to better develop and analyze simulation models.
• A comprehensive review is given for the basic probability and statistics needed for the remainder of the book.
• There are more than 217 examples, 301 figures (16 in color), and 220 homework problems to facilitate the learning process.
• A comprehensive set of support materials is provided for professors who are using the book to teach a simulation course.