GRADUATE SCHOOL
M.SC. In Industrial Engineering (With Thesis)
MATH 671 | Course Introduction and Application Information
| Course Name |
Fuzzy Optimization
|
|
Code
|
Semester
|
Theory
(hour/week) |
Application/Lab
(hour/week) |
Local Credits
|
ECTS
|
|
MATH 671
|
Fall/Spring
|
3
|
0
|
3
|
7.5
|
| Prerequisites |
None
|
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| Course Language |
English
|
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| Course Type |
Elective
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| Course Level |
Third Cycle
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| Mode of Delivery | - | |||||
| Teaching Methods and Techniques of the Course | - | |||||
| Course Coordinator | - | |||||
| Course Lecturer(s) | ||||||
| Assistant(s) | - | |||||
| Course Objectives | Fuzzy Set Theory is the approach to solve the problems that cannot be solved by classical set theory or probability theory. In this course, Fuzzy Set Theory and the basis of fuzyy logic will be examined. It also describes, fuzzy logic applications such as fuzzy control and fuzzy decision making, disucced in the areas of optimization. |
| Learning Outcomes |
The students who succeeded in this course;
|
| Course Description | The course covers basic concepts and applications of Fuzzy Set Theory. |
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|
Core Courses | |
| Major Area Courses | ||
| Supportive Courses | ||
| Media and Management Skills Courses | ||
| Transferable Skill Courses |
WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES
| Week | Subjects | Related Preparation |
| 1 | Fuzzy Sets Basic Definitions | Fuzzy Sets, Uncertainity, and Information by G.J. Klir and T.A. Folger, Prentice Hall, Inc. |
| 2 | Fuzzy Sets Basic Definitions | Fuzzy Sets, Uncertainity, and Information by G.J. Klir and T.A. Folger, Prentice Hall, Inc. |
| 3 | Fuzzy Measures and Fuzziness Measurements | Fuzzy Sets, Uncertainity, and Information by G.J. Klir and T.A. Folger, Prentice Hall, Inc. |
| 4 | Fuzzy Measures and Fuzziness Measurements | Fuzzy Sets, Uncertainity, and Information by G.J. Klir and T.A. Folger, Prentice Hall, Inc. |
| 5 | Fuzzy Relations and Fuzzy Graphics | Fuzzy Sets, Uncertainity, and Information by G.J. Klir and T.A. Folger, Prentice Hall, Inc. |
| 6 | Fuzzy Relations and Fuzzy Graphics | Fuzzy Sets, Uncertainity, and Information by G.J. Klir and T.A. Folger, Prentice Hall, Inc. |
| 7 | Possibility Theory, Probability Theory and Fuzzy Set Theory | Fuzzy Sets, Uncertainity, and Information by G.J. Klir and T.A. Folger, Prentice Hall, Inc. |
| 8 | Possibility Theory, Probability Theory and Fuzzy Set Theory | Fuzzy Sets, Uncertainity, and Information by G.J. Klir and T.A. Folger, Prentice Hall, Inc. |
| 9 | Fuzzy Logic | Fuzzy Sets, Uncertainity, and Information by G.J. Klir and T.A. Folger, Prentice Hall, Inc. |
| 10 | Midterm | Fuzzy Sets, Uncertainity, and Information by G.J. Klir and T.A. Folger, Prentice Hall, Inc. |
| 11 | Decision Makig in Fuzzy Environment | Fuzzy Sets, Uncertainity, and Information by G.J. Klir and T.A. Folger, Prentice Hall, Inc. |
| 12 | Decision Makig in Fuzzy Environment | Fuzzy Sets, Uncertainity, and Information by G.J. Klir and T.A. Folger, Prentice Hall, Inc. |
| 13 | Decision Makig in Fuzzy Environment | Fuzzy Sets, Uncertainity, and Information by G.J. Klir and T.A. Folger, Prentice Hall, Inc. |
| 14 | Decision Makig in Fuzzy Environment | Fuzzy Sets, Uncertainity, and Information by G.J. Klir and T.A. Folger, Prentice Hall, Inc. |
| 15 | Review | Fuzzy Sets, Uncertainity, and Information by G.J. Klir and T.A. Folger, Prentice Hall, Inc. |
| 16 | Review of the Semester |
| Course Notes/Textbooks | Some chapters and exercises of the above books will be used. |
| Suggested Readings/Materials | Fuzzy Logic with Engineering Applications by T.J. Ross, McGrawHill Book Company, 1995. |
EVALUATION SYSTEM
| Semester Activities | Number | Weigthing |
| Participation | ||
| Laboratory / Application | ||
| Field Work | ||
| Quizzes / Studio Critiques | ||
| Portfolio | ||
| Homework / Assignments | ||
| Presentation / Jury |
1
|
30
|
| Project | ||
| Seminar / Workshop | ||
| Oral Exams | ||
| Midterm |
1
|
30
|
| 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
|
6
|
90
|
| Field Work |
0
|
||
| Quizzes / Studio Critiques |
0
|
||
| Portfolio |
0
|
||
| Homework / Assignments |
0
|
||
| Presentation / Jury |
1
|
0
|
|
| Project |
1
|
30
|
30
|
| Seminar / Workshop |
0
|
||
| Oral Exam |
0
|
||
| Midterms |
1
|
30
|
30
|
| Final Exam |
1
|
30
|
30
|
| Total |
228
|
COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP
|
#
|
Program Competencies/Outcomes |
* Contribution Level
|
||||
|
1
|
2
|
3
|
4
|
5
|
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| 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. |
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| 2 | To be able to identify, formulate and solve Industrial Engineering-related problems by using state-of-the-art methods, techniques and equipment. |
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| 3 | To be able to use techniques and tools for analyzing and designing industrial systems with a commitment to quality. |
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| 4 | To be able to conduct basic research and write and publish articles in related conferences and journals. |
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| 5 | To be able to carry out tests to measure the performance of industrial systems, analyze and interpret the subsequent results. |
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| 6 | To be able to manage decision-making processes in industrial systems. |
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| 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. |
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| 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. |
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| 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. |
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| 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. |
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*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest