Linear Optimization(IND 501)
| Course Code | Course Name | Semester | Theory | Practice | Lab | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| IND 501 | Linear Optimization | 1 | 3 | 0 | 0 | 3 | 6 |
| Prerequisites | |
| Admission Requirements |
| Language of Instruction | English |
| Course Type | Compulsory |
| Course Level | Masters Degree |
| Course Instructor(s) | EBRU ANGÜN ebru.angun@gmail.com (Email) |
| Assistant | |
| Objective |
This course introduces basic theoretical principles and algorithms of linear programming, which provide a foundation for the other mathematical programming concepts and techniques. Furthermore, the course also introduces several different types of mathematical models, which can be used to model real-life applications, and the softwares GAMS and CPLEX, which can be used to solve large-scale linear programming problems. The objectives of the course are determined as follows: • Introduce how to formulate mathematical models of the real-life applications • Enable students to apply the linear optimization algorithms efficiently • Enable students to use the softwares GAMS and CPLEX for large-scale linear optimization problems • Facilitate the understanding of the theory of the other mathematical programming techniques |
| Content | |
| Course Learning Outcomes |
Upon successful completion of this course, the student will be able to LO 1: Formulate real-life problems through mathematical models. LO 2: Exemplify different types of the mathematical programming problems. LO 3: Select the linear optimization algorithm which solves a specific problem most efficiently. LO 4: Distinguish the differences among the linear optimization algorithms. LO 5: Explain the problems that can be encountered while solving linear optimization problems and their solution procedures. LO 6: Define some of the common concepts in linear and nonlinear optimization. LO 7: Define some of the common concepts in deterministic and stochastic linear optimization. LO 8: Analyze the results of linear programming problems. LO 9: Solve large-scale linear programming problems through Professional softwares. LO 10: Formulate real-life applications through different types of models and conclude which model suits best for the specific application |
| Teaching and Learning Methods | |
| References |
Bazaraa, M.S., Jarvis, J.J., Sherali, H.D., “Linear Programming and Network Flows”, 4. Edition, Wiley, New Jersey, 2010 Bertsimas, D., Tsitsiklis, J.N., “Introduction to Linear Optimization”, Athena Scientific Series in Optimization and Neural Computation, Massachusetts, 1997 Bazaraa, M.S., Sherali, H.D., “Nonlinear Programming: Theory and Algorithm”, 3. Edition, Wiley, New Jersey, 2006 Wolsey, L.A., “Integer Programming”, Wiley, New Jersey, 1998 GAMS Manual, downloadable from http://www.gams.com/ |
Theory Topics
| Week | Weekly Contents |
|---|
Practice Topics
| Week | Weekly Contents |
|---|
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 | ||||||
| 2 | ||||||
| 3 | ||||||
| 4 | X | |||||
| 5 | X | |||||
| 6 | X | |||||
| 7 | X | |||||
| 8 | ||||||
| 9 | X | |||||
| 10 | X | |||||
| 11 | X | |||||
| 12 | ||||||
| Activities | Number | Period | Total Workload |
|---|---|---|---|
| Total Workload | 0 | ||
| Total Workload / 25 | 0,00 | ||
| Credits ECTS | 0 | ||