Organization of the Book

Chapter 1 is an introduction to the field of simulation modeling. After Chapter 1 the student should know what simulation is and be able to put the different types of simulation into context. Chapter 2 introduces the basics of random number generation and random variate generation within the context of the KSL library.

Chapter 3 introduces problem solving and statistical concepts related to Monte Carlo simulation experiments. Chapter 4 introduces the important concept of how a discrete-event clock “ticks” and sets the stage for process modeling using activity diagramming. Finally, simple (but comprehensive) examples of KSL event modeling are presented.

Chapter 5 presents important concepts of statistical analysis that occur within discrete-event simulation modeling. This chapter should provide a refresher for students on statistical concepts. Chapter 6 dives deeper into process-oriented modeling. Important concepts within process-oriented modeling (e.g. entities, attributes, activities, state variables, etc.) are emphasized within the context of a number of examples. In addition, a deeper understanding of the KSL is developed including flow of control and input/output. After finishing Chapter 6, students should be able to model interesting systems from a process viewpoint using the KSL. Chapter 7 presents more advanced concepts within simulation and especially how the KSL facilitates the modeling. In particular, non-stationary arrivals and resource staffing are introduced in Chapter 7, as well as constructs for more advance modeling with resources. Chapter 8 presents more advanced techniques used within Monte Carlo methods.

The Appendix A and Appendix B are extremely useful for understanding the concepts of random variate generation and distribution modeling. For undergraduate students, I recommend starting with Appendices A and B. Appendix C provides an overview of queueing theory, which can be useful when verifying and validating the results of simulation models involving queues. The remaining appendices provide information on probability distributions and statistical tables.

Future chapters are planned for when new KSL functionality is developed.

  1. Preface
  2. Chapter 1 Simulation Modeling
  3. Chapter 2 Modeling Randomness
  4. Chapter 3 Monte Carlo Methods
  5. Chapter 4 Introduction to Discrete Event Modeling
  6. Chapter 5 Analyzing Simulation Output
  7. Chapter 6 Process View Modeling
  8. Chapter 7 Advanced Event and Process View Modeling
  9. Chapter 8 Advanced Monte Carlo Methods
  10. Appendices
  • Appendix A Generating Pseudo-Random Numbers and Random Variates
  • Appendix B Probability Distribution Modeling
  • Appendix C Queueing Theory
  • Appendix D KSL Utility Packages
  • Appendix E.1 Discrete Distributions
  • Appendix E.2 Continuous Distributions
  • Appendix F Statistical Tables
  1. References G

Depending on the level of programming skill of the students, instructors should be able to cover chapters 1 through 6 within a semester course.