To learn the tools and techniques for analyzing complex business situations to make scientific decisions
Prerequisite(s)
-
Corequisite(s)
-
Special Requisite(s)
Attendance is mandatory; students who have attended less than 60% of classes will receive zero points from the term project regardless of their exam grades. Each student is expected to actively participate in classroom discussions and exercises.
Instructor(s)
Assist. Prof. Dr. Duygun Fatih Demirel
Course Assistant(s)
Schedule
Thursday, 13:00-15:45
Office Hour(s)
Tuesday, 11:00-11:45, Office 2-A-10
Teaching Methods and Techniques
-Lecture, question-answer, discussion, problem solving
Principle Sources
* R.T. Clemen and T. Reilly, Making Hard Decisions with DecisionTools, 2nd edition, Duxbury.
* H.A. Taha, Operations Research: An Introduction, 10th edition, Prentice Hall.
* W.L. Winston, Operations Research: Applications and Algorithms, 4th edition, Cengage Learning
* M. Peterson, An Introduction to Decision Theory. Cambridge University Press.
Other Sources
-
Course Schedules
Week
Contents
Learning Methods
1. Week
Introduction to Decision Theory, Decision Making under Certainty, Introduction to MCDM
Lecture, question-answer, discussion, problem solving
2. Week
AHP
Lecture, question-answer, discussion, problem solving
3. Week
TOPSIS
Lecture, question-answer, discussion, problem solving
4. Week
Goal Programming
Lecture, question-answer, discussion, problem solving
5. Week
Fuzzy AHP
Lecture, question-answer, discussion, problem solving
6. Week
Fuzzy TOPSIS
Lecture, question-answer, discussion, problem solving
7. Week
Ramadan Festival (Holiday)
-
8. Week
Midterm Exam
-
9. Week
MOORA
Lecture, question-answer, discussion, problem solving
10. Week
Decision Making under Uncertainty, Decision Making under Risk, Introduction to Decision Trees
Lecture, question-answer, discussion, problem solving
11. Week
Decision Trees: Cumulative Risk Profiles,
Lecture, question-answer, discussion, problem solving
12. Week
Decision Trees: Multiobjective Problems in Decision Trees Analysis
Lecture, question-answer, discussion, problem solving
Lecture, question-answer, discussion, problem solving
14. Week
Project Presentations
Lecture, question-answer, discussion
15. Week
Project Presentations
Lecture, question-answer, discussion
16. Week
Final
-
17. Week
Final
-
Assessments
Evaluation tools
Quantity
Weight(%)
Midterm(s)
1
35
Quizzes
3
5
Project(s)
1
15
Final Exam
1
45
Program Outcomes
PO-1
Knowledge about management processes and management skills
PO-2
Knowledge and application skills related to the methods and competencies required for solving engineering problems
PO-3
Knowledge about developing areas of manufacturing and service sectors
PO-4
Ability to work in multi-disciplinary engineering teams
PO-5
Experience and knowledge of scientific research and publishing within the frame of academic ethics
Learning Outcomes
LO-1
Knows the elements of the decision problem, and can make decisions according to different criteria by creating a decision tree.
LO-2
Distinguishes between prior and posterior probabilities, calculate posterior probabilities in decisions where sample information exists and decide whether sample information is required.
LO-3
Has general knowledge about multi-criteria decision-making methods, and applies AHP, TOPSIS, Goal Programming, and MOORA techniques to real-life problems.
LO-4
Makes use of fuzzy set theory when necessary to solve multi-criteria decision-making problems.
LO-5
Knows the usage of sensitivity analysis and applies it properly.
