SYST 302: Systems Methodology and Design

Professor Terry L. Friesz

email: tfriesz@gmu.edu

HOMEWORK and EXAMS: Homework will be assigned and randomly graded; homework will be the primary source of questions for the exams. There will be two midterm exams and a final exam. The exams will be in-class exams. See “Special Information” below.

GRADING: Your homework will constitute 25% of you grade; each of the three exams will constitute 25%. You will also be expected to participate in classroom discussions; students who are unprepared for such discussions will have points deducted from their exams/homework average score.

RECOMMENDED TEXT: The following text is recommended but by no means required:

1. Blanchard and Fabrycky: Systems Engineering and Analysis, 3rd edition, Prentice-Hall, 1998

REQUIRED SOFTWARE: You must have Matlab. You must learn how to use the following special features of MatLab: the Optimization Toolbox, the ODE solver, the PDE solver and Simulink.

IMPRORTANCE OF LECTURES: Lectures will be the single most important source of information. Although there are no explicit penalties for missing a lecture, students who miss the lectures will likely be unable to fully comprehend the material and will likely do extremely poorly on exams. Much of each classroom presentation is extemporaneous and geared to the particular difficulties of the class on the day of the lecture; as a consequence your own personal notes are of great importance.

SPECIAL INFORMATION: No makeup exams will be given, unless you have a written excuse approved by the undergraduate Associate Dean. Such excuses are very seldom given. So don’t even think about asking for a make-up exam if you do not already have such written approval from the Associate Dean.

OUTLINE

1. key types of mathematical problems encountered in systems analysis and design

2. foundations of mathematical programming

3. microeconomics: theory of the consumer

4. microeconomics: theory of the firm

5. equilibria and games

6. economic planning and market design

7. review of ordinary differential equations

8. transform methods

9. traditional engineering economics

10. example mathematical programming applications

11. foundations of dynamic optimization

12. example dynamic optimization applications

13. capital budgeting

14. project time phasing

15. network design

16. a first look at partial differential equations

17. elements of probability theory and stochastic processes

18. reliability and stochastic programming

19. a first look at stochastic differential equations

20. financial engineering