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IE 510 | Course Introduction and Application Information
| Course Name |
Discrete Optimization
|
|
Code
|
Semester
|
Theory
(hour/week) |
Application/Lab
(hour/week) |
Local Credits
|
ECTS
|
|
IE 510
|
Fall/Spring
|
3
|
0
|
3
|
7.5
|
| Prerequisites |
None
|
|||||
| Course Language |
English
|
|||||
| Course Type |
Elective
|
|||||
| Course Level |
Second Cycle
|
|||||
| Mode of Delivery | - | |||||
| Teaching Methods and Techniques of the Course | - | |||||
| National Occupation Classification | - | |||||
| Course Coordinator | ||||||
| Course Lecturer(s) | ||||||
| Assistant(s) | - | |||||
| Course Objectives | Purpose of this course is to give the students an understanding and experience about discrete optimizaton problems, related concepts and exact and approximate solution techniques. |
| Learning Outcomes |
The students who succeeded in this course;
|
| Course Description | Formulation of integer and combinatorial optimization problems. Optimality conditions and relaxation. Polyhedral theory and integer polyhedra. Computational complexity. The theory of valid inequality, strong formulations. Duality and relaxation of integer programming problems. General and special purpose algorithms including branch and bound, decomposition, and cutting-plane algorithms. |
|
|
Core Courses | |
| Major Area Courses |
X
|
|
| Supportive Courses | ||
| Media and Management Skills Courses | ||
| Transferable Skill Courses |
WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES
| Week | Subjects | Related Preparation | Learning Outcome |
| 1 | Introduction | ||
| 2 | Optimality, Relaxation and Bounds | ||
| 3 | Optimality, Relaxation and Bounds | ||
| 4 | Well-solved Cases: Network Flows, Shortest Path, Optimal Trees, Matching and Assignments | ||
| 5 | Well-solved Cases: Network Flows, Shortest Path, Optimal Trees, Matching and Assignments | ||
| 6 | Branch and Bound Methods | ||
| 7 | Branch and Bound Methods | ||
| 8 | Midterm exam | ||
| 9 | Cutting Plane Algorithms: Valid Inequalities, Theory and Practice | ||
| 10 | Cutting Plane Algorithms: Valid Inequalities, Theory and Practice | ||
| 11 | Cutting Plane Algorithms: Valid Inequalities, Theory and Practice | ||
| 12 | Dynamic Programming | ||
| 13 | Approximation Algorithms | ||
| 14 | Approximation Algorithms | ||
| 15 | General Review and Evaluation | ||
| 16 | General Review and Evaluation |
| Course Notes/Textbooks | Instructor notes and lecture slides |
| Suggested Readings/Materials | Integer Programming. Laurence A. Wolsey, Wiley, 1998. ISBN: 0471283665 Integer and Combinatorial Optimization. Laurence A. Wolsey, George L. Nemhauser, Wiley, 1999. ISBN: 047182819X |
EVALUATION SYSTEM
| Semester Activities | Number | Weigthing |
| Participation | ||
| Laboratory / Application | ||
| Field Work | ||
| Quizzes / Studio Critiques |
|
|
| Portfolio | ||
| Homework / Assignments |
1
|
20
|
| Presentation / Jury | ||
| Project | ||
| Seminar / Workshop | ||
| Oral Exams | ||
| Midterm |
1
|
40
|
| Final Exam |
1
|
40
|
| Total |
| Weighting of Semester Activities on the Final Grade |
60
|
|
| Weighting of End-of-Semester Activities on the Final Grade |
40
|
|
| Total |
ECTS / WORKLOAD TABLE
| Semester Activities | Number | Duration (Hours) | Workload |
|---|---|---|---|
| Theoretical Course Hours (Including exam week: 16 x total hours) |
16
|
3
|
48
|
| Laboratory / Application Hours (Including exam week: '.16.' x total hours) |
16
|
0
|
|
| Study Hours Out of Class |
15
|
4
|
60
|
| Field Work |
0
|
||
| Quizzes / Studio Critiques |
|
0
|
|
| Portfolio |
0
|
||
| Homework / Assignments |
1
|
60
|
60
|
| Presentation / Jury |
0
|
||
| Project |
0
|
||
| Seminar / Workshop |
0
|
||
| Oral Exam |
0
|
||
| Midterms |
1
|
27
|
27
|
| Final Exam |
1
|
30
|
30
|
| Total |
225
|
COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP
|
#
|
PC Sub | Program Competencies/Outcomes |
* Contribution Level
|
||||
|
1
|
2
|
3
|
4
|
5
|
|||
| 1 |
To have an appropriate knowledge of methodological and practical elements of the basic sciences and to be able to apply this knowledge in order to describe engineering-related problems in the context of industrial systems. |
-
|
-
|
-
|
-
|
X
|
|
| 2 |
To be able to identify, formulate and solve Industrial Engineering-related problems by using state-of-the-art methods, techniques and equipment. |
-
|
-
|
-
|
X
|
-
|
|
| 3 |
To be able to use techniques and tools for analyzing and designing industrial systems with a commitment to quality. |
-
|
X
|
-
|
-
|
-
|
|
| 4 |
To be able to conduct basic research and write and publish articles in related conferences and journals. |
-
|
-
|
-
|
-
|
X
|
|
| 5 |
To be able to carry out tests to measure the performance of industrial systems, analyze and interpret the subsequent results. |
-
|
-
|
-
|
X
|
-
|
|
| 6 |
To be able to manage decision-making processes in industrial systems. |
-
|
-
|
-
|
-
|
X
|
|
| 7 |
To have an aptitude for life-long learning; to be aware of new and upcoming applications in the field and to be able to learn them whenever necessary. |
-
|
-
|
X
|
-
|
-
|
|
| 8 |
To have the scientific and ethical values within the society in the collection, interpretation, dissemination, containment and use of the necessary technologies related to Industrial Engineering. |
-
|
-
|
-
|
X
|
-
|
|
| 9 |
To be able to design and implement studies based on theory, experiments and modeling; to be able to analyze and resolve the complex problems that arise in this process; to be able to prepare an original thesis that comply with Industrial Engineering criteria. |
-
|
-
|
X
|
-
|
-
|
|
| 10 |
To be able to follow information about Industrial Engineering in a foreign language; to be able to present the process and the results of his/her studies in national and international venues systematically, clearly and in written or oral form. |
-
|
-
|
-
|
X
|
-
|
|
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest
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