| INDUSTRIAL ENGINEERING (ENGLISH, THESIS) | |||||
| Master | TR-NQF-HE: Level 7 | QF-EHEA: Second Cycle | EQF-LLL: Level 7 | ||
| Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
| EEE5010 | Optimization | Fall | 3 | 0 | 3 | 6 |
| The course opens with the approval of the Department at the beginning of each semester |
| Language of instruction: | En |
| Type of course: | Departmental Elective |
| Course Level: | |
| Mode of Delivery: | Face to face |
| Course Coordinator : | Prof. Dr. SÜREYYA AKYÜZ |
| Course Lecturer(s): |
Prof. Dr. SÜREYYA AKYÜZ |
| Course Objectives: | To equip students with the mathematical theory of optimization and solution methods. |
|
The students who have succeeded in this course; Students will - be able to formulate optimization problems - understand basic differences between various constraints - apply numerical techniques to solve optimization problems |
| Optimization as a decision making problem. Optimization over an open set. Optimization under equality constraints; Lagrange multipliers. Optimization under inequality constraints. Linear programming. Numerical methods. |
| Week | Subject | Related Preparation | |
| 1) | The Optimization Problem. Examples. | ||
| 2) | Mathematical preliminaries | ||
| 3) | Mathematical preliminaries | ||
| 4) | The Weierstrass Theorem. Application to example problems. | ||
| 5) | Optimization over an open set. Necessary and sufficient conditions. | ||
| 6) | Numerical techniques: Gradient algorithm, Newton's method. | ||
| 8) | Optimization with equality constraints. Lagrange multipliers. | ||
| 9) | Optimization with inequality constraints. Kuhn-Tucker conditions. | ||
| 10) | Linear programming: Standard maximization and minimization problems. | ||
| 11) | Linear programming: Primal and dual problems. Duality theorem. Optimality conditions. | ||
| 12) | The Simplex algorithm. | ||
| 13) | Discrete dynamic programming. | ||
| 14) | Large optimization problems. Decomposition methods. | ||
| Course Notes: | 1. P. Varaia, Lecture Notes on Optimization, web |
| References: | 1. C.T. Kelley, Iterative Methods for Optimization, SIAM |
| Semester Requirements | Number of Activities | Level of Contribution |
| Attendance | % 0 | |
| Laboratory | % 0 | |
| Application | % 0 | |
| Field Work | % 0 | |
| Special Course Internship (Work Placement) | % 0 | |
| Quizzes | % 0 | |
| Homework Assignments | 5 | % 25 |
| Presentation | % 0 | |
| Project | % 0 | |
| Seminar | % 0 | |
| Midterms | 1 | % 25 |
| Preliminary Jury | % 0 | |
| Final | 1 | % 50 |
| Paper Submission | % 0 | |
| Jury | % 0 | |
| Bütünleme | % 0 | |
| Total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 50 | |
| PERCENTAGE OF FINAL WORK | % 50 | |
| Total | % 100 | |
| Activities | Number of Activities | Workload | |
| Course Hours | 14 | 42 | |
| Laboratory | |||
| Application | |||
| Special Course Internship (Work Placement) | |||
| Field Work | |||
| Study Hours Out of Class | 16 | 136 | |
| Presentations / Seminar | |||
| Project | |||
| Homework Assignments | 5 | 10 | |
| Quizzes | |||
| Preliminary Jury | |||
| Midterms | 1 | 2 | |
| Paper Submission | |||
| Jury | |||
| Final | 1 | 2 | |
| Total Workload | 192 | ||
| No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
| Program Outcomes | Level of Contribution | |
| 1) | Process view and analytic thinking | |
| 2) | managerial thinking with technical background | |
| 3) | To have theoretical knowledge on operations research. | |
| 4) | Awareness about the applications of operations research | |
| 5) | To have ability of selection and efficient use of modern techniques, equipments and information technologies for industrial engineering | |
| 6) | To be capable of designing and conducting experiments and collecting data, analyzing and interpreting results | |
| 7) | To have verbal and oral effective communication skills by using visual methods in Turkish and English | |
| 8) | To be aware of entrepreneurship, sustainability and innovation | |
| 9) | To lead disciplinary and multi-disciplinary teams, to develop solution approaches in complex situations, to work individually and to take responsibility. | |
| 10) | To have conscious of professional and ethical responsibility | |
| 11) | To have conscious of necessity to lifelong learning | |
| 12) | To be aware of economic and legal implications of engineering solutions | |
| 13) | Economic, social and environmental responsibility while solving management problems |