ECTS @ IUE ECTS @ IUE ECTS @ IUE ECTS @ IUE ECTS @ IUE ECTS @ IUE ECTS @ IUE

Syllabus ( IE 583 )

   Basic information
Course title: Multi Criteria Optimization and Performance Assessment
Course code: IE 583
Lecturer: Assoc. Prof. Dr. Kemal SARICA
ECTS credits: 7.5
GTU credits: 3 (3+0+0)
Year, Semester: 1/2, Fall and Spring
Level of course: Second Cycle (Master's)
Type of course: Area Elective
Language of instruction: English
Mode of delivery: Face to face
Pre- and co-requisites: none
Professional practice: No
Purpose of the course: An introduction to multi-criteria decision making (MCDM) is provided in this course. As the the branches of MCDM
multi-objective goal programming and optimization are introduced including mathematical programming approaches.
Solution methodologies and algorithms are discussed in the light of pareto optimality. Based on MCDM, Data Envelopment
Analysis (DEA) will be introduced to assess multi input output systems performance. Basic CCR and BCC DEA models
will be covered up and their differences will be discussed beside the possible uses in production, financial and socio-economic systems.
At the end of this course, you will
• understand and model multi-objective goal programming problems.
• understand multi-objective optimization approaches
• know basic MCDP solution methodologies and algorithms.
• be able to understand pareto-optimality.
• have basic understanding of multi input output performance assessment
• know basic Data Envelopment Analysis models and related concepts
   Learning outcomes Up

Upon successful completion of this course, students will be able to:

  1. Be able to comprehend multi-objective goal programming problems and convert the problems in

    Contribution to Program Outcomes

    1. Increase his/her knowledge level about Operations Research, Management Sciences and Production Management.
    2. Propose alternative point of views by analyzing complex industrial problems methodically

    Method of assessment

    1. Written exam
    2. Homework assignment
    3. Term paper
  2. Identify multi-objective optimization approaches

    Contribution to Program Outcomes

    1. Develop solutions that yield optimal outputs by using limited resources efficiently
    2. Propose alternative point of views by analyzing complex industrial problems methodically
    3. Become an authority in modeling, simulation, process management and related subjects.

    Method of assessment

    1. Written exam
    2. Homework assignment
    3. Term paper
  3. Implement basic MCDP solution methodologies and algorithms

    Contribution to Program Outcomes

    1. Develop solutions that yield optimal outputs by using limited resources efficiently
    2. Become an authority in modeling, simulation, process management and related subjects.

    Method of assessment

    1. Written exam
    2. Homework assignment
    3. Term paper
  4. Identify pareto-optimality

    Contribution to Program Outcomes

    1. Increase his/her knowledge level about Operations Research, Management Sciences and Production Management.
    2. Develop solutions that yield optimal outputs by using limited resources efficiently
    3. Become an authority in modeling, simulation, process management and related subjects.

    Method of assessment

    1. Written exam
    2. Homework assignment
    3. Term paper
  5. Identify basics of multi input-output performance assessment

    Contribution to Program Outcomes

    1. Increase his/her knowledge level about Operations Research, Management Sciences and Production Management.
    2. Become an authority in modeling, simulation, process management and related subjects.

    Method of assessment

    1. Written exam
    2. Homework assignment
    3. Term paper
  6. Identify and implement basic Data Envelopment Analysis models

    Contribution to Program Outcomes

    1. Develop solutions that yield optimal outputs by using limited resources efficiently
    2. Become an authority in modeling, simulation, process management and related subjects.

    Method of assessment

    1. Written exam
    2. Homework assignment
    3. Term paper
   Contents Up
Week 1: Introduction
Week 2: Goal Programming - Mathematical modelling
Week 3: Goal Programming - Solution approaches, Homework 1
Week 4: Multi-objective mathematical modeling
Week 5: Multi-objective optimization objective characteristics, Homework 2
Week 6: Pareto optimality
Week 7: Economic Interpretation of pareto optimality - Midterm
Week 8: Multi-objective optimization solution methods - Weighting, Constraint, Homework 3
Week 9: Multi-objective optimization solution methods - Multi-objective simplex
Week 10: Introduction to performance assessment, Homework 4
Week 11: Multi input-output systems assessment
Week 12: Desired inputs and outputs
Week 13: CCR and BCC efficiency models
Week 14: Scale efficiency - Midterm
Week 15*: -
Week 16*: Final, Project Submission
Textbooks and materials: Collette, Yann, and Patrick Siarry. Multiobjective optimization: principles and case
studies. Springer, 2003.
Recommended readings: Trzaskalik, Tadeusz, and Jerzy Michnik, eds. Multiple objective and goal programming:
recent developments. Vol. 12. Springer, 2002.
Cooper, William Wager, Lawrence M. Seiford, and Kaoru Tone. Data envelopment
analysis: a comprehensive text with models, applications, references and DEA-solver
software. Springer, 2007
  * Between 15th and 16th weeks is there a free week for students to prepare for final exam.
Assessment Up
Method of assessment Week number Weight (%)
Mid-terms: 7, 14 40
Other in-term studies: 0
Project: 16 20
Homework: 3, 5, 8, 10 10
Quiz: 0
Final exam: 16 30
  Total weight:
(%)
   Workload Up
Activity Duration (Hours per week) Total number of weeks Total hours in term
Courses (Face-to-face teaching): 3 14
Own studies outside class: 4 14
Practice, Recitation: 0 0
Homework: 6 4
Term project: 4 5
Term project presentation: 0 0
Quiz: 0 0
Own study for mid-term exam: 10 2
Mid-term: 2 2
Personal studies for final exam: 7 2
Final exam: 3 1
    Total workload:
    Total ECTS credits:
*
  * ECTS credit is calculated by dividing total workload by 25.
(1 ECTS = 25 work hours)
-->