| Week | Section | Subject |
| 1 | 1.1 | Introduction to Modeling and Simulation, |
| 1.2 | Systems, Models, and Simulation |
| 1.3 | Discrete Event Simulation; Time-Advance Mechanisms |
| 2 | | Components and Organization of a Discrete-Event Simulation Model |
| 3 + 4 | Appendix 1B | Basic concepts of Stochastic Process and Queuing systems |
| 5 | 1.4 | Simulating a Single-Server Queuing System |
| 6 | 1.7 | Steps in a Sound Simulation Study |
| 1.8.3 | Monte Carlo Simulation |
| 7 | 1.9 | Advantages, Disadvantages, and Pitfalls of Simulation |
| 8 | Chapter 4 | Review of Basic Probability and Statistics |
| 5.2 | Building Simulation Models |
| 5.2 | Guidelines for Determining the Level of Model Details |
| 9 | 5.3 | Verification of a Simulation Model |
| 5.4 | Validating a Simulation Model |
| 5.6 | Comparing Real-World Observations with the Simulation
Output Data |
| 10 | Chapter 6 | Selecting Input Probability Distribution: |
| | Hypothesizing Family of Distributions, |
| | Estimation of Parameters, |
| | Goodness of Fit. |
| 11 | Chapter 7 | Random Number Generations: Techniques, |
| | Methods for Testing Pseudo Random Numbers. |
| 12 | Chapter 8 | Generating Random Variates with Arbitrary Distributions, |
| | Other Methods for Random Variates Generation. |
| 13 | | Generation of arrival processes: Poisson Process, |
| | Non-stationary Poisson Process. |
| 14 | Chapter 9 | Output Analysis: Transient and Steady-State Simulation. |
| | Estimating Means, Building Confidence Interval, |
| 15 | | Multiple Replications, Batch Means Method. |