This book is about stochastic Petri nets (SPNs), which have proven to be a popular tool for modelling and performance analysis of complex discrete-event stochastic systems. The focus is on methods for modelling a system as an SPN with general firing times and for studying the long-run behavior of the resulting SPN model using computer simulation. Modelling techniques are illustrated in the context of computer, manufacturing, telecommunication, workflow, and transportation systems. The simulation discussion centers on the theory that underlies estimation procedures such as the regenerative method, the method of batch means, and spectral methods.Tying these topics together are conditions on the building blocks of an SPN under which the net is stable over time and specified estimation procedures are valid. In addition, the book develops techniques for comparing the modelling power of different discrete-event formalisms. These techniques provide a means for making principled choices between alternative modelling frameworks and also can be used to extend stability results and limit theorems from one framework to another. As an overview of fundamental modelling, stability, convergence, and estimation issues for discrete-event systems, this book will be of interest to researchers and graduate students in Applied Mathematics, Operations Research, Applied Probability, and Statistics. This book also will be of interest to practitioners of Industrial, Computer, Transportation, and Electrical Engineering, because it provides an introduction to a powerful set of tools both for modelling and for simulation-based performance analysis. Peter J. Haas is a member of the Research Staff at the IBM Almaden Research Center in San Jose, California. He also teaches Computer Simulation at Stanford University and is an Associate Editor (Simulation Area) for Operations Research.

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