CS 5420 : Techniques of Simulation

CS 5420: Techniques of Simulation

Semester Hours:   3.0
Contact Hours:   3
Coordinator:   Hassan Rajaei
Text:   Discrete-Event-System Simulation
Author(s):   Banks, Carson, Nelson, Nicol
Year:   2009

SPECIFIC COURSE INFORMATION

Catalog Description

This course introduces students to the principles and practices of modern computer-based simulation. The course covers mathematical methods, modeling techniques, simulation methodologies, and analysis of simulation results, with applications in various fields. Students will design, implement, and evaluate simulation systems using modern tools and programming languages.

Prerequisites: Full Admission to MS in CS program or consent of department.

Course type: ELECTIVE

SPECIFIC COURSE GOALS

• I can compare the types of simulations such as Monte Carlo, Discrete Event, and/or Real-time simulation.
• I can select simulation types of application areas (e.g. industry, defense, finance, and education).
• I can build a simulation model using the appropriate simulation tool.
• I can create what-if scenarios with variable number of input parameters and configurations.
• I can analyze the results obtained from a simulation study
• I can analyze relevant research and communicate my findings

LIST OF TOPICS COVERED

• Introduction (1 week)
  • What is Modeling & Simulation
  • Continuous and Discrete
  • Applications
  • Benefits and Limitations
• Simulation Basics (2 weeks)
  • Basic Concepts
  • State Variables
  • Simulation Process and Activities
  • Model building
  • Evaluation and Analysis
• Stages of Simulation Model Development (3 weeks)
  • Problem Analysis
  • Conceptual Model
  • Simulation Model
  • Level of Details
  • Input Data and Output Results
  • Remodeling and Scenarios
  • Verification and Validation
• Abstract Representation Techniques (1 week)
  • Hierarchal Modeling
  • State-Transition Diagrams
  • Graphical User Interface
  • Animation and Visualization
• Simulation Tools (1 week)
  • General Purpose vs. Dedicated
  • Simulation Kernel
  • Library Modules
  • Data Collection and Output Analysis Supports
• Random Number Distribution (1 week)
  • Sources of Randomness
  • Random Number Generation
  • Random Number Distribution
  • Random Variates
  • Mean Value Analysis
• Evaluation of Simulation Modeling (2 weeks)
  • Verification: Making the Correct Model
  • Validation: Is the Model Correctly Built
  • Input and Output Analysis
  • Testing the Model and the Results
  • Accreditation and Acceptance
• Term Project (4 weeks)