İzmir Ekonomi Üniversitesi
  • TÜRKÇE

  • 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
    Course Language
    English
    Course Type
    Elective
    Course Level
    Third Cycle
    Mode of Delivery -
    Teaching Methods and Techniques of the Course -
    National Occupation Classification -
    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;

    • Be able to examine the Set Theory problems.
    • Be able to interpret the systems which include fuzzines within the scope of fuzzy set theory .
    • Be able to combine the information of decision theory and the information of fuzzy set theory.
    • Be able to improve the proof techniques of Fuzzy Set Theory.
    • Be able to solve problems that include uncertainty with using Fuzzy Set Theory.
    Course Description The course covers basic concepts and applications of Fuzzy Set Theory.

     



    Course Category

    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 Learning Outcome
    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

    #
    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.

    -
    -
    -
    -
    -
    2

    To be able to identify, formulate and solve Industrial Engineering-related problems by using state-of-the-art methods, techniques and equipment.

    -
    -
    -
    -
    -
    3

    To be able to use techniques and tools for analyzing and designing industrial systems with a commitment to quality.

    -
    -
    -
    -
    -
    4

    To be able to conduct basic research and write and publish articles in related conferences and journals.

    -
    -
    -
    -
    -
    5

    To be able to carry out tests to measure the performance of industrial systems, analyze and interpret the subsequent results.

    -
    -
    -
    -
    -
    6

    To be able to manage decision-making processes in industrial systems.

    -
    -
    -
    -
    -
    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.

    -
    -
    -
    -
    -
    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.

    -
    -
    -
    -
    -
    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.

    -
    -
    -
    -
    -
    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.

    -
    -
    -
    -
    -

    *1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest

     


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