Topic and Assignment [Referenced readings prior to class].
Problem solving and the role of simulation. Systems, models and modeling. Simulation forms: Monte Carlo, discrete event, continuous, combined, hybrid. Focus: discrete event simulation
(DES). Consideration of uses: analysis and acquisition, education and training, entertainment, research. Simulation in the small: a manual example. Simulation in the large: the difficulties. The simulation model life cycle. Dissection of the manual example: what is needed for DES.
Introduction of the
Visual Simulation Environment
). Classes this week are to be conducted in the CS Laboratory in McBryde 118 from 8:00 to 9:15 a.m.(the scheduled meeting time). The VSE software will be provided to students on a CD. The VSE Teacher and the on-line User’s Manual are excellent sources; the former for examples of model development and the latter for detailed explanation.. [VSE Teacher]
Probability and statistics: background and review. Input data modeling. Distribution fitting. Model representation: simulation programming. From implementation to specification: the Conical
Methodology, an object-oriented representational approach. World views: classical taxonomy, beyond the classical taxonomy. [1, 2,
Random number generators (RNG). What is meant by
“randomness”? Answers and “pseudo-answers.” Methods for
RNG. Qualities and characteristics of RNG. Implementation issues and considerations. [RNG Handout, 4]
Tests for randomness: theoretical and empirical. Basic concerns with period length and statistical sampling. Considerations of efficiency, portability, and robustness. [RNG Handout, 5]
Process generators or transformation to non-uniform random variates. General methods: direct inversion, approximate inversion (table lookup), rejection. Specific transformations for selected distributions, both discrete and continuous. Completion of discussion of process transformations and RNG. [RNG Handout]
6 October Model representation. Terminology. Data structures for dynamic description. Reconsideration of the world views. Implementation issues. Time flow mechanisms and run-time efficiency. Combined discrete event and continuous models. Needs and justification for combined modeling. 
13 October Verification and validation techniques and responsibilities.
Software versus model development: similarities and differences.
Model accreditation. Model certification. 
20 October Statistical analysis of simulation output. Identification of the basic problems. Terminating or finite-horizon versus steady-state estimation. Time or event basis of measurement. Initial state definition. Replications and batch means analysis. [8, pp. 77-94]
27 October Alternative simulation approaches: virtual reality and the creation of virtual environments. Guest lecturers for both class sessions.
3 November Output analysis. The autocorrelation problem and effect of sample dependency. Overview of an alternative method to replications and batch means for steady-state estimation: the regenerative process. [8, pp. 94-125]
10 November Variance reducing techniques. Common random numbers.
Importance or rare event sampling. Practical considerations.
Completion of output analysis. 
17 November Alternative simulation approaches: system dynamics. Systems
“thinking.” Introduction to system dynamics modeling, a simulation approach with different objectives. Information feedback and time lags in decision making. [SD Handout, 10]
Alternative simulation approaches: (1) simulation/stimulation for system test and integration, (2) qualitative simulation, and (3) agent based modeling and simulation.
8 December Review for Final Exam. Submission of course project.