Course Description

Principles of simulation and application of simulation languages to both continuous and discrete systems. Prerequisites: CS 2020 and MATH 2470 or equivalent.

Course Syllabus

1. Introduction
   1. Simulation Language Tutorial
   2. Overview of Basic Approaches
      1. Monte Carlo Simulation
      2. Queuing Network Simulation
      3. Quasi-continuous Simulation
      4. Discrete-event Simulation <--- Emphasized
   3. Applications
2. Simulation Basics
   1. Basic Concepts:
      System, Attribute, State, Event, Process, Activity, Time, Parallelism, Model
   2. Basic Mechanisms:
      Time Advance, Scheduling
   3. Alternative Models:
      Activity, Process, Others
3. Stages of Simulation Model Development
   1. Problem
   2. System Analysis
   3. Data Collection
   4. Fitting Distributions to Data:
      1. Parameter Estimation
      2. Goodness-of-Fit
   5. Iterative Prototyping
   6. Statistical Instrumentation
   7. Verification
   8. Validation <--- Emphasized
   9. Inference
4. Abstract Representation Techniques
   1. Activity-cycle Diagrams
   2. State-Transition Diagrams
   3. Petri Nets
5. Random Number Distributions
   1. Sources of Randomness
   2. Random Number Generation
   3. Random Variates
      1. From Distribution Functions
      2. From Empirical Data
6. Evaluation of Simulation Modeling
   1. Ethical Issues in Simulation
   2. Benefits of Simulation
   3. Limitations of Simulation

Course Requirements