CISC 627: Simulation of Discrete Systems

Catalog Description:
Modeling and computer simulation of systems characterized by stochastic discrete events. Study and use of discrete system simulation languages.

Current Text:
Discrete Event System Simulation
Banks, J. and Carson, J.S.,1984
Prentice Hall Publishing Company

Goals:
The objective of this course is to present the rudiments of discrete event simulation analysis in a clear and thorough fashion. Simulation methodology draws on computer science, statistics, and operations research and is now sufficiently developed and coherent to be called a discipline in its own right. Simulation sometimes appears deceptively easy, but following through the course will reveal unexpected depths. Many simulation studies are statistically defective and many simulation programs are inefficient. It is expected that at the end of the course the student should have sufficient knowledge to simulate effectively.

Contents:

• Systems, Models and Simulation (Definitions, Classifications)
• Developing Simulation Models (Event Representation of the System, Advancing Time in a Simulation Model, Event and Memory Management in Simulation Models)
• Simulation Languages for Modeling
• Mathematical and Statistical Models (Probability Distributions, Poisson Process)
• Analytic Models and Simulation: Analytic State-Change Models (Markov Chains), Analytic Congestion Models- Queueing Systems (Characteristics, Steady State Behavior, Little's Result, M/G/1 Queue, M/M/1, M/M/s, and M/D/1 Queues, Queueing Networks)
• Generation of Random Numbers and Stochastic Variates (Random Number Generators, Statistical Tests for Random Numbers, Inverse Transform Technique)
• Output Analysis (Point and Interval Estimation of Performance Measures, Output Analysis for Terminating Simulations, Confidence Intervals with Specified Accuracy, Output Analysis for Steady State Simulations: Initialization Bias, Replication Method, Batch Means for Interval Estimation, Regenerative Simulation, Variance Reduction Methods)
• Model Experimentation and Optimization: Comparison and Evaluation of Alternative System Designs (Comparison of Two/Several System Designs, Statistical Models for Estimating the Effect of Design Alternatives)
• Uses of Simulation (Monte Carlo Integration, Importance Sampling, Random Search Algorithms, Simulated Annealing)

Required Background: CISC 220 (Data Structures).

Helpful Background: A knowledge of elementary probability theory.