IT 735 / OR 735 / SYST 735

Advanced Stochastic Simulation

Spring 2009

Important Announcements & Deadlines

Instructor: Dr. Chun-Hung Chen
Email: cchen9@gmu.edu
Office: Science & Tec II, Room 319
Phone: 703-993-3572
Fax: 703-993-1521
Office Hours: Monday 4:00 - 6:00 PM

Course Description:

This class basically is an advanced version and an extension of the basic simulation class OR 635 Discrete System Simulation.  The extension includes both depth and breadth. In the depth part, we will cover the advanced materials which are not included in OR 635 course. Examples include advanced random variate generation, advanced input/output analysis, and variance reduction techniques. In the breadth part, we will study several useful simulation topics beyond the basics in OR 635. Examples include rare-event simulation, importance sampling, bootstrapping, Quasi Monte Carlo simulation, agent-based modeling, etc.

Since this is a doctoral-level class, in addition to regular lectures, this class will include extensive literature study and research project. Students will get a bit taste of doctoral study. For Master students, this class gives you a chance to see what a Ph.D. study looks like. For Ph.D. students, this class should better prepare yourself for doing research.

Students will conduct a small-scale research term project. The focus of these projects is "simulation-based decision making". Simulation is a popular tool for designing large, complex, stochastic systems, since closed-form analytical solutions generally do not exist for such problems.  While the advance of new technology has dramatically increased computational power, efficiency is still a big concern when using simulation for stochastic optimization, in which case many alternative designs must be simulated.  A decision maker is forced to compromise on simulation accuracy, modeling accuracy, and the optimality of the selected design.  This class will discuss different approaches to address this issue.  Students will investigate and/or develop efficient simulation-based optimization techniques in the term projects.

Prerequisite: Students in the this class are assumed to have the background of an introductory simulation class such as OR 635 Discrete System Simulation, or permission from the instructor.

Grading: Homework 15%; Special Topic Study 30%; Project Proposal 5%; Project Presentations 15%; Term Project Report 30%; Class Participation 5%.

Required Text: A. M. Law & W. D. Kelton, "Simulation Modeling & Analysis" (same as OR 635), any edition of this book is fine.

General Rules:

  1. Late homework and term project report is always allowed. No need to get advanced permission. However, the penalty for late homework and term project report is 25% for the first day and then 5% per day. No exemption.
  2. Turning in HW through email is subject to a 20% penalty.
  3. No collaborations are allowed for homework, although discussions are encouraged.
  4. Comments are strongly encouraged.
  5. No cheating.

Course Outline

1. Simulation Fundamentals and Advances:

 

Topics

Reading Assignment

A

Advanced Random Variate Generation & Input Modeling

·         Alias method

·         Poisson Process

·         Non-stationary process

·         Correlated random numbers

 Chapters 7 & 8

B

Queueing Theory for Simulation Verification

Appendix 1 & Chapter 5

C

Simulation Methodologies

·         Standard Clock Method

·         Monte Carlo Integration

Handout-SC & Section 1.8

D

Advanced Output Analysis

·         Determination of simulation runs

·         Correlated output

Chapter 9

E

Variance Reduction Techniques

Chapter 11

F

Comparing Alternative Systems

Chapter 10 & Handout-APCS

G

Efficient Simulation Sampling Technique for Optimization -- OCBA

Handout-OCBA1 & Handout-OCBA2

 

2. Special Topic Study & Presentation

Some possible topics are listed below; but not limited to the list. Each student has to select a group of topics to study and present in the class. The schedule of 2009 presentations is as follow.

Date

Topics

Presenter

2/16

- Fluid Dynamic Simulation

Arun

3/2

- Petri Net

Mark

3/16

- Agent-based Modeling & Simulation

Tony

3/23

- Design of Experiments

- Validation & Verification

Timothy V.

3/30

- Quasi Monte Carlo Simulation

- Latin Hypercube Sampling

- Bootstrapping and Jackknifing for Accessing Variability using Limited Data

Tim H.

4/6

- Rare Event Simulation 

- Importance Sampling

- Stratified Sampling

- Variance Reduction Techniques

Ben

 A good starting point for your study is our text book and the Winter Simulation Conference Proceedings.

Please identify a paper (or prepare a report) which can give a good introduction and overview of the topic you study, and email the paper to the class at least one week before your presentation. You can also consider to send another paper which give more in-depth discussions.   

Each student gives a presentation. The length of presentation is around 90 ~ 100 minutes not including Q&A. Please email your presentation to the instructor at least 24 hours in advance.  In your presentation, please consider to include the following items:

·        Introduction

·        Basic ideas & fundamentals

·        Theoretical development if any

·        What are the strengths and weaknesses?

·        What are the state-of-the-art?

·        Where and how to apply?

It is very important to show rigorous and quantitative results in the presentation. However, it is even important to well explain the ideas and intuitions of the fundamentals. It is not a good idea to give too much extensive mathematical formula. Figures and animation are always welcome. We want both depth and breath. Backups are useful too.

The paper and presentation will be graded by the instructor and the class.  All students are required to read the paper before presentation and so will be able to ask good in-depth questions at the presentation.

3. Term Project:

Theme: Efficient Simulation-based Decision Making

Students are expected to investigate a technique for efficient simulation-based decision making. One possible approach is to integrate the efficient simulation techniques with optimization methodology.  Students have to meet with the instructor personally in the projects to ensure right progress and discuss potential research questions. Students will give presentations to the class about their techniques at the end of the semester.

Homework Assignments & Handouts:

Useful Links:

Dr. Chen is serving on the editorial boards and organizing/program committees for the following journals/conferences:

==>>>   You are highly encouraged to submit your contributions ~~~~~

Go to Professor Chun-Hung Chen's Page