| Course Type | Elective Course |
|---|
| Course Level | Bachelor's Degree |
|---|
| Year of Study | - |
|---|
| Semester | - |
|---|
| ECTS | 5 |
|---|
| Name of Lecturer(s) |
|
| Learning Outcomes |
At the end of this course, the students;
- Understand the decision-making process
- Understand the conditions for pareto-optimization
- Evaluate and model the multi-criteria decisions
- Have an ability to use modern solution approaches for multi-criteria decision problems
- Learn the advantage of the softwares at the solution stage of the problems
|
| Mode of Delivery | Face to face |
|---|
| Prerequisites | No |
|---|
| Recommended Optional Programme Components | Mathematics, especially Linear Algebra and Operations Research |
|---|
| Course Description | This course is one of the basic sections of Operations Research which studies the methods of decision making in real life situations when a choosing of the best alternative(s) among more than one due to the several criteria is necessary.The content of the course includes modeling and investigating the real life multi-criteria problems, evaluation and comparison methods for multi-criteria decisions, utility function theory and Analytic Hierarchy Process approaches, multi-objective optimization problems, order relations in vector spaces, various concepts of solutions in vector optimization, scalarization approaches for convex and nonconvex problems, and goal programming topics. |
|---|
| Course Contents |
| WEEK |
TOPICS |
|
1st Week |
Basic concepts. What is the decision making? Alternatives, Criteria. |
|
2nd Week |
Decision variables and criteria spaces and transformations between them. |
|
3rd Week |
Examples on multicriteria problems. Analytic Hierarchy Process (AHP) approaches in decision making. |
|
4th Week |
Basic concepts of AHP, the consistency of comparison matrix, |
|
5th Week |
Examples of Multi-Objective optimizatişon problems |
|
6th Week |
Solution aproaches to the Multicriteria Decision Making |
|
7th Week |
Scalarization of the objectives |
|
8th Week |
Goal programming. Linear problems. Geometrical interpretation |
|
9th Week |
Midterm |
|
10th Week |
Preemptive goal programming models. |
|
11th Week |
Aplications of the goal programming problems. |
|
12th Week |
Concept of Pareto Optimality, Trade-of curves |
|
13th Week |
Comprimise programming |
|
14th Week |
Computer aplications. |
|
| Recommended or Required Reading | Wayne L. Winston, Operations Research. Applications and Algorithms, Third edition, Duxbury Press, Belmont, California, 1994,
Ronald R. Rardin (Purdue University), Optimization in Operations Research,Prentice-Hall, Inc., Upper Saddle River, New Jersey, 1998.
Ders Notları
|
|---|
| Planned Learning Activities and Teaching Methods |
- Lecture
- Problem Solving
- Practice
- Presentation
|
|---|
| Assessment Methods and Criteria |
|
Quantity |
Percentage (%) |
| Mid-term |
1 |
30 |
| Assignment |
1 |
20 |
| Practice |
1 |
15 |
| Attendance |
1 |
5 |
| Total(%) |
|
70 |
| Contribution of In-term Studies to Overall Grade(%) |
|
70 |
| Contribution of Final Examination to Overall Grade(%) |
|
30 |
| Total(%) |
|
100 |
|
| ECTS Allocated Based on Student Workload |
| Activities |
Quantity |
Duration (Hour) |
Total Workload (Hour) |
| Midterm exam |
1 |
2 |
2 |
| Preparation for Quiz |
|
|
|
| Individual or group work |
14 |
2 |
28 |
| Preparation for Final exam |
1 |
25 |
25 |
| Course hours |
14 |
4 |
56 |
| Preparation for Midterm exam |
1 |
25 |
25 |
| Laboratory (including preparation) |
|
|
|
| Final exam |
1 |
2 |
2 |
| Homework |
1 |
8 |
8 |
| Total Work Load | 146 |
| Total Work Load / 30 (h) | 4.86 |
| ECTS Credit of the Course | 5 |
|
| Language of Instruction | Turkish |
|---|
| Work Placement(s) | No |
|---|
