CS 470
/ Course Coordinator / Mateen RizkiCourse Title /
System Simulation
/ Total Credits / 4Catalog Description
Introduction to simulation and comparison with other techniques. Discrete simulation models. Introduction to queuing theory and stochastic processes. Comparison of simulation languages. Simulation methodology and selected applications. 3 hours lecture.
Textbook
A. M. Law and W. D. Kelton, Simulation Modeling and Analysis (3nd Edition), McGraw-Hill, 2000.
Language
MODSIM and SIMSCRIPT programming manuals by CACI Inc. (available online)
Course Goals
The student should have learned the following:
1. Difference between continuous and discrete simulation.
2. The relationship between a real system, model, and simulation.
3. The operation of random number generators.
4. How to select input distributions to drive a simulation.
5. How to design a simulation experiment.
6. How to evaluate the output of a simulation.
The students should be able to apply these concepts and techniques to:
1. Analyze a real system and formulate a model.
2. Design and implement either a continuous or discrete event simulation.
3. Identify input distributions for simulations using real system data.
4. Conduct simulation experiments and interpret the results
5. Present a report that addresses a specific question using the results of the simulation.
Prerequisites by Topic
1. Knowledge of basic probability and statistics.
2. Familiarity with object oriented programming in C++
3. Knowledge of basic data structures including lists and queues
4. Ability to use software tools such as spreadsheets and plotting packages
Major Topics Covered in the Course
Basic Simulation Concepts L&K 1.1-3, 1.6-9
Object Oriented Simulation L&K 1.4-1.5 (C version only)
Discrete Event Simulation and the Simscript / ModSim Languages
Simulation Methodologies L&K 2.6-2.8
Probabilistic Aspects of Simulation
Review of Probability L&K 4
Random Number Generators L&K 7
Random Variates L&K 8.1, 8.2, 8.3.1-8,
8.3.15-16, 8.4, 8.6
Statistical Aspects of Simulation
Selecting Input Distributions L&K 6
Analysis of Output L&K 9.1-4, 10.1-2
Experimental Design L&K 12.1-12.3
Simulation Languages L&K 3
Project Presentations
Programming Projects
There are thee programming projects for the course.The first two projects are designed to familiarize students with the issues related to system simulation. The first project involves a continuous systems simulation while the second involve a discrete event simulation. Students are expected to design experiments, collect results, and present a short written report documenting their experiments and recommending a course of action. The students choose a third project with the instructor’s approval. This project involves posing a question about a real system, developing a model, measuring real data, fitting distributions, conducting experiments, performing statistical analysis, writing a report, and making an oral presentation.
Estimate CSAB Category Content
Core / Advanced / Core / AdvancedData Structures / Concepts of PL / 1
Algorithms / 1 / Comp Organization + Architecture
Software Design / 2 / Other
Oral and Written Communications
Students must submit a report with each project that discusses their experimental design, presents experimental results, and addresses a question posed by the instructor. The first two reports are typically 3-6 pages in length. The final report is typically longer and the student must give an oral presentation of their final project.
Social and Ethical Issues
The improper use of system simulation is discussed. Techniques for recognizing flawed experiments that produce invalid or misleading conclusions are outlined.
Theoretical Content
A probability and statistics are reviewed. The basis for random number generation and various random distributions are discussed. Techniques for estimating parameters with desired confidence levels are discussed. Simple queuing theory is reviewed.
Problem Analysis
Students are required to pose questions and then develop models to address their questions. Students must learn to identify the static and dynamic structure of a real system of interest and determine the type of model best suited to describing their problem. Issues related to model decomposition and the process of formalizing a model and translating it into a simulation program are discussed in class and student apply these techniques to their problem.
Solution Design
The students must be able to take a model and collect real data, fit distributions, implement a simulation program, conduct experiments, and perform statistical analysis of the results Students are expected to be able implement models as simulation programs in both general-purpose programming languages as well as specialized simulation languages. The emphasis is on the process of design and analysis.