CSC 454 - 3 Semester Hours

System Simulation and Modeling

Simulation, modeling and problem-solving techniques; discrete event and continuous change models; simulation languages; simulation applications.

CSC 241, MAT 215 and at least one upper-level programming course.

The course provides students with the basic knowledge and experience necessary to utilize computer simulation as a tool for system modeling and problem solving. The technical aspects of constructing and analyzing a simulation model will be discussed, along with the implementing the basic components of a discrete-event simulator. The students will be exposed to a wide variety of computer simulation applications.

Upon successful completion of this course, students will be able to:

1. demonstrate proficiency in modeling a real-life system, including data gathering, input and output analysis.
2. write a discrete-event simulator.

1. Continuous Time Simulation
   1. Examples of Physical Systems
   2. Approximation by Finite Differences
2. Introduction to Discrete-Event System Simulation
   1. Simulation examples: queueing and inventory systems
   2. Concepts discrete-event simulation: event list, event-scheduling, and time advance algorithms
3. Statistical Models in Simulation
   1. Discrete and continuous distributions
   2. Poisson process
   3. Empirical distributions
   4. Useful statistical models
4. Queueing Models
   1. Characteristics of queueing systems and queueing notation
   2. Long-run measurements of performance
   3. Steady state behaviors of infinite- and finite-population models
5. Random Numbers
   1. Properties of random number generators
   2. Techniques for generation of pseudo-random numbers
   3. Tests for random numbers
6. Random Variate Generation
   1. Inverse Transform technique
   2. Direct transformation for the normal and lognormal distributions
   3. Convolution method
   4. Acceptance-rejection technique
7. Analysis of Simulation Data
   1. Input modeling: data collection, identification of the distribution, parameter estimation, goodness-of-fit tests.
   2. Verification and validation of simulation models: model building, verification, calibration and validation of models.
   3. Output analysis: measures of performance and their estimation, terminating and steady-state simulations

1. Lectures.
2. Discussion.

Assigned readings, homework, papers, programs, and projects.

1. Homework, exams, programming assignments, programming project, modeling project

No additional resources needed.

J. Banks (ed). Handbook of Simulation: Principles, Methodology, Advances, Applications, and Practice. John Wiley, New York, 1998.

J. Banks, J.S. Carson, II, B.L. Nelson and D.M. Nicol. Discrete-Event System Simulation. Prentice Hall, New Jersey, 2000.

C. Hanell, B.K. Ghosh and R Bowden. Simulation Using ProModel. McGraw Hill, Boston, 2000.

W.D. Kelton, R.P.Sadowski and D.A. Sadowski. Simulation with Arena. McGraw Hill, Boston, 1997.

A.M. Law and W.D. Kelton. Simulation Modeling and Analysis, (3rd ed). McGraw Hill, Boston, 2000.

S.M. Ross. Simulation. Academic Press, MA, 1997.

J.R. Thompson. Simulation: A Modeler's Approach. John Wiley and Sons, New York, 1999.