Mathematical Programming(EM 512)
Course Code | Course Name | Semester | Theory | Practice | Lab | Credit | ECTS |
---|---|---|---|---|---|---|---|
EM 512 | Mathematical Programming | 1 | 4 | 0 | 0 | 3 | 8 |
Prerequisites | |
Admission Requirements |
Language of Instruction | English |
Course Type | Compulsory |
Course Level | Masters Degree |
Course Instructor(s) | E. Ertuğrul KARSAK ekarsak@gsu.edu.tr (Email) |
Assistant | |
Objective | The objective of the course is to familiarize the student with mathematical modeling and the basic tools of mathematical programming to the extent they can be applied in operational situations. The material is quite practical and will prove useful in personal decision making as well as in many types of engineering and management decisions. |
Content |
- Introduction and organization - Overview of modeling - Introduction to linear programming - Graphical solution - Linear programming model - Assumptions of linear programming - Additional modeling examples - The simplex method - Standard form of linear programming - Unrestricted variables - Simplex method in tabular form - Artificial starting solution - The big M method - The two-phase method - Special cases in simplex method application - Degeneracy - Unbounded solutions - Multiple optimal solutions - Infeasible solution - Post-optimality analysis - Presentation of LINDO software - Duality - Definition of the dual problem - Primal-dual relationships - Dual simplex method - Sensitivity analysis - The transportation problem - The assignment problem - Introduction to network models - Terminology of networks - The minimum spanning tree problem - Goal programming - Deterministic dynamic programming - Characteristics of dynamic programming problems - Principle of optimality - Selected dynamic programming applications |
Course Learning Outcomes |
LO 1: Mathematical modeling LO 2: Linear programming and simplex method LO 3: Transportation and assignment models LO 4: Network models LO 5: Goal programming LO 6: Deterministic dynamic programming |
Teaching and Learning Methods | Lecture; problem sessions; discussion; self study. |
References |
- Hillier, F.S., Lieberman, G.J., Introduction to Mathematical Programming, McGraw-Hill, 1995. - Taha, H.A., Operations Research: An Introduction, Sixth edition, Prentice-Hall, 1997. |
Theory Topics
Week | Weekly Contents |
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Practice Topics
Week | Weekly Contents |
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Contribution to Overall Grade
Number | Contribution | |
---|---|---|
Contribution of in-term studies to overall grade | 0 | 0 |
Contribution of final exam to overall grade | 0 | 0 |
Toplam | 0 | 0 |
In-Term Studies
Number | Contribution | |
---|---|---|
Assignments | 0 | 0 |
Presentation | 0 | 0 |
Midterm Examinations (including preparation) | 0 | 0 |
Project | 0 | 0 |
Laboratory | 0 | 0 |
Other Applications | 0 | 0 |
Quiz | 0 | 0 |
Term Paper/ Project | 0 | 0 |
Portfolio Study | 0 | 0 |
Reports | 0 | 0 |
Learning Diary | 0 | 0 |
Thesis/ Project | 0 | 0 |
Seminar | 0 | 0 |
Other | 0 | 0 |
Toplam | 0 | 0 |
No | Program Learning Outcomes | Contribution | ||||
---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | ||
1 | X | |||||
2 | X | |||||
3 | X | |||||
4 | X | |||||
5 | X | |||||
6 | X | |||||
7 | X | |||||
8 | X | |||||
9 | X | |||||
10 | X | |||||
11 | X | |||||
12 | X |
Activities | Number | Period | Total Workload |
---|---|---|---|
Total Workload | 0 | ||
Total Workload / 25 | 0,00 | ||
Credits ECTS | 0 |