Instructor: Dr. James R. Holt

429 SE 13th Court

Gresham, OR 97080

(jholt@wsu.edu)

Home Office: (503) 669-6676

Call After 7:00 AM and before 9:30 PM

James R. Holt, Ph.D., PE., is an Associate Professor of Engineering Management at Washington State University - Vancouver. He teaches Operations Research, Constraints Management, Statistics, Engineering Economics, Simulation and other special topics. He consulted for three years with Management Advisory Group, Inc. and the Avraham Y. Goldratt Institute. Prior to his consulting work, he was Department Head, Engineering and Environmental Management at the Air Force Institute of Technology, Wright-Patterson AFB, Ohio. Dr. Holt retired from the Air Force with 20 years experience in engineering, computer and technology management. He has published several articles on project management, maintenance and artificial intelligence. He holds a BS in Mechanical Engineering (Utah State University), an MS in Facilities Engineering (Air Force Institute of Technology) and a Ph.D. specializing in Industrial Engineering / Business Administration (Texas A&M University).

 Background:

Engineer managers frequently face difficult problems that are too complex to be solved by traditional analytical techniques. Sometimes these problems are too important to solve them with ‘seat of the pants’ decisions. These problems usually involve the interaction of many different processes that are highly variable in nature. While it may be theoretically possible to model such problems mathematically, in many cases, it is just too costly (in money or time). And at the same time, it is not feasible to the perform necessary multiple 'design of experiments' tests in reality. In such situations, computer simulation modeling becomes a very potent and attractive alternative.

Simulation helps the engineer manager better understand 'the system'. The understanding comes from formulating the model and from experimenting with the model. Both actions develop a solid grasp of the relationships between components and the interactive linkages. Simulation allows the manager to include uncertainty in the analysis and to quickly perform 'what if' analyses to test decision options. Often, a simple simulation offers tremendous insight into a very complex situation.

Good simulation models are simpler to understand than comprehensive mathematical models. This means they have a broader appeal and more power to demonstrate and convince difficult audiences. By changing the parameters and observing the general response (video game mode), the casual observer can see the sensitivity of key factors and the near irrelevance of other actions.

Course Description:

This introductory course is applied simulation taught at the graduate level. It is also a system analysis course. Students learn how to analyze systems and how to represent them in the simulation model. Students are expected to bring topics and problems to class and to contribute in significant discussion about the material. This is a hands-on course. Students are taught simulation theory through practice in developing more and more complex models. The course includes a range of simulation styles including: basic manual simulation (rolling dice, random number tables); simple automated simulation (use of general purpose software like BASIC, spreadsheets, macros); traditional simulation (coded programs with tabular results); real time monitoring (graphic displays during simulation); and state-of-the-art object oriented software (including two and three dimensional animation).

Many simulations will be reviewed in class. Students are expected to create many small simulations relevant to their environment and to create at least one significant (large) simulation model during the course. The real success of this course will depend upon the student and the students personal involvement in fitting the simulation tools to reality.

Prerequisites:

Students should have a very solid understanding of algebra as a minimum and an awareness of the concepts of math through differential equations. A basic course in statistics (such as STAT 430) prior to this simulation course is important. Experience in computer programming in a formal programming language or the use of extensive macros is very desirable. Students without the algebra, without understanding of basic distributions and stochastic processes and without experience in computer programming can still succeed but will be at a major disadvantage. Students concerned about these recommended prerequisites should discuss their situation with the instructor.

This is a programming course. The majority of the programming in this course will be in a very high level, object oriented software tool (pull down menu and fill in the blank). Consequently, it is possible to create substantial simulations without understanding computer code or being a formal programmer. The high level software allow quick creation of models and minimizes the requirement for queuing theory and statistics.

Required Text: Simulation with Arena, Second Edition

By W. David Kelton, Randall P. Sadowski, and Deborah A. Sadowski

McGraw-Hill

ISBN 0-07-239270-3, 2002 which includes

Required Software: Arena 5.0+

Student software comes with the text.

Additional Software:

While there is no other specific software required for this course. Some advanced students may choose to use Visio or Visual Basic to enhance their models. The majority of the course will be using the Student Versions of Arena.

Other References:

There are many good books on simulation. Most large libraries have a variety of usable texts. Some suggested referennces are: 

1. Simulation Modeling and Analysis, Averal M. Law and W. David Kelton, McGraw Hill 1992, ISBN 0-07-036698-5
2. Simulation Made Easy, Charles Harrell and Kerim Tumay, Engineering and Management Press 1994, ISBN 0-89806-136-9 
3. Industrial Dynamics, Jay W. Forrester, MIT Press, 1961, ISBN 0-262-06003
4. Study Notes in System Dynamics, Michael R. Goodman, Wright-Allen Press 1974, ISBN 0-914700-00-6

Methodology:

The method of teaching will be through lecture, demonstration, laboratory and self discovery. Lecture on theory is by example. The strengths and weaknesses of simulation will be found by practice. Students will be assigned simulation problems during each class. Students are encouraged to work together and solve the problems in many different ways. Periodically, students will be asked to demonstrate their solutions.

Students will also be exposed to advanced simulation techniques. Topics such as random number generation, generating random deviates and experimental design will be introduced. However, these tools are now so well integrated into modern software tools that they are transparent to most users.

The course outline follows a logical progression leading to a solid foundation and degree of confidence in simulation. Students will be encouraged to progress at their own speed. Quick student may move ahead of the body of the class. This gives the fast learner the opportunity to investigate tools in much more depth. Students with limited previous exposure to computer programming can still succeed by becoming involved in student groups and receiving out-of-class assistance.

Homework Approach:

Each class period will investigate some aspects of simulation. Students are expected to continue the investigation on their own time. Each week (almost) students are assigned a simple homework to demonstrate some specific simulation skill. The homework models can be very simple and similar to the examples shown in class or in the text (They should be different enough from the textbook examples to show understanding of the skill). STUDENTS SHOULD NOT BE LIMITED TO THE SCOPE OF THE ASSIGNED HOMEWORK! . Students should stretch their learning to meet their own desires and the applicability in their work environment. Students are expected to share the insight they gain with the class any time during the course. Selected contributions will be posed to the class web site. Towards the end of the course, students will demonstrate their draft Term Projects.

Term Project:

All students will complete a substantial term simulation. The Term Project is developed during the term as the student gains experience. The Term Project does not have to demonstrate any specific skills. Rather, the Term project demonstrates the application of simulation toward a specific problem of interest to the student. The students should very carefully verify and validate the Term Project Model. The final submission should document appropriate 'What-if?' analyses and formulate proposed changes (or management improvements) for the system analyzed. The Term Project should result in increased understanding of the problem and a recommended action to solve the problem or improve performance. The Term Project should provide value to the student and the organization analyzed.

 Grading Approach:

There are no exams in this course. Students are required to submit homework and the Term Project project. Grading will be based upon the submissions. Submissions must meet the minimum standard established by the instructor to be acceptable. Unacceptable projects will be re-accomplished or given partial points. Under this grading approach, every student should be able to achieve 100% of the available points. Individual student success far and beyond the minimum standards will be posted or otherwise be made available for other students to review.

Segment Projects	Points Allowed
Dice Game 1 Dice Game 2	5 5
Single Server Model	5
Balanced Linear Flow	5
Assembly/Disassembly Model	10
Statistical Test Model	10
Animation Model	5
Interactive Simulation	5
Draft Term Project	15
In-class Presentation	5
Term Project Model	30
Total Points	100

 Grade Ranges:

Grades are awarded as follows: A: 100-93, A-: 92-85, B+: 84-80, B: 80-75, I: 75 and below.

 Other Comments:

In graduate courses, participation is critical to the success of the class. It is especially so in this class. While the class is not a seminar based discussion group, each student is expected to contribute thoughtful comment and question. The insights gained by students studying similar systems are the most fruitful part of this learning process. Although participation is not a direct contributing factor in the course evaluation equation, it will be used against students who are not involved in the group learning process. The maximum adjustment for non-participation will be - 5%.

Treatment of Sensitive Material: The students of this class come from many companies. While you will be amazed how often different companies suffer from the same types of problems, some companies are reluctant to make public their concerns and breakthrough solutions. In addition, some students may be dealing with sensitive or propietary materials within their company. We have academic freedom within the classroom, we do not mean to embarass any student. And academic freedom does not mean we have a secure environment even with password protection. If you have sensitive material that should not be shared publicly, please do not discuss it in class or other class communications channels.

If you indicate you have sensitive material in your homework submissions, I will not share that information with any other people. Some firms desire a non-disclosure agreement in this cases. Such agreements are easily possible for sensitive work. However, most of the time, a simple "sensitve material" statement is all you need.

Incomplete Policy:Occationally, events beyond the students control prevent the student from completing all the required work for the Major Application Project. This project is represents the meat of the course and demonstrates the student has adequately learned the course material. If a student has completed at least through the FRT (representing more than 50% of the course material) but has not completed the full project by the time grades are do, the student will be awarded an 'I' - Incomplete grade. Students recieving an Incomplete should try to complete their project within the following semester.

Disability: Reasonable accommodations are available for students who have a documented disability. Please notify the course instructor during the first two weeks of class of any accommodations needed for the course. Late notification may cause the requested accommodations to be unavailable. All accommodations must be approved through the Associate Director of Student Services.

Plagerism: Cases of academic dishonesty shall be processed in accordance with the Academic Integrity Policy, as printed in the Student Handbook and the Faculty Manual and as available from the Office of Student Affairs.http://www.studentaffairs.wsu.edu/hb_standards.asp.

Class Number Date	Reading Sim w/Arena	Focus Topic	Lecture / Technical Topics	Training Topic	Assignments (All due the following week)
1 Jan 16	Chap 1	What is Simulation?	Methods and Models	Dice Game 1	Document Dice Game Results
2 Jan 23	Chap 2	Fundamental Simulation Concepts	Tracking Time, Manipulating Time, Queuing, Variability	Dice Game 2	Document Dice Game Results
3 Jan 30	Chap 3,4	Running Your First Simple Arena Model	More Queuing Theory	When to Simulate / When Not to Simulate	Create Single Server Model
4 Feb 6	Chap 5 Appendix C,D	Modeling Basics Operations and Inputs	Modeling Basic Statistics Distributions	How to design a model	Create Balanced Line of Five Servers
5 Feb 13	Chap 5	Modeling Basics Representing Reality	More Stochastic Processes Random Number Generators Distribution Confirmation	Practical Data Collection	Create Assembly / Disassembly Model
6 Feb 20	Chap 6	Intermediate Modeling and Terminal Analysis	Evaluating System Performance - Monitoring System Variables	Interpreting Output Data - Traditional Simulation	Statistical Test Model
7 Feb 27	Chap 6	Intermediate Modeling and Terminal Analysis	Animation		Animation Model
8 Mar 6	Chap 7	Transfer Mechanisms Steady State Conditions	Stabilizing a model
9 Mar 20	Chap 8	Detailed Modeling: Fine Tuning	System Verification Does the model work as I suspected?	Answering What-If questions	Interactive Simulation
10 Mar 27	Chap 9	Advanced Modeling Issues and Techniques	System Validation Does the model parallel my world?	Distribution Systems Visio Integration Advanced Animation	Draft Term Project
11 Apr 3	Chap 10	Customization and Integration	Other Arena Modules Integrating Visual Basic	Intimate software interfaces
12 Apr 10	Chap 11	Advanced Statistical Issues	Systems Management: Managing Complex Systems: Control Mechanisms	Developing Effective Test Models	Selected Student Simulations
13 Apr 17	Chap 12	Conducting Statistical Studies	Systems Management: Managing Complex Systems: Flows for A, V, T, I Plants	Statistical Analysis of Output	Selected Student Simulations
14 Apr 24	Handout	Simulation Games	Suggesting Systems Improvements: Presenting the Results	Student Simulations	Final Term Project
15 May 5		Finals Week NO CLASS THIS WEEK!	MAY 5 is LAST DAY to turn in final project	Students not turning in their Term Project by 1 May well receive an 'I' grade!

 Spring Term 2003

EM 517, Simulation Modeling of Engineering Systems

Dr. James R. Holt

URL: http://www.vancouver.wsu.edu/fac/holt/em517syl.htm