|
| Course Description |
|
| Course Name |
: |
Modern Heuristics |
|
| Course Code |
: |
EM-557 |
|
| Course Type |
: |
Optional |
|
| Level of Course |
: |
Second Cycle |
|
| Year of Study |
: |
1 |
|
| Course Semester |
: |
Fall (16 Weeks) |
|
| ECTS |
: |
6 |
|
| Name of Lecturer(s) |
: |
Instructor CENK ŞAHİN
|
|
| Learning Outcomes of the Course |
: |
Learns how to use meta heuristic techniques in engineering
Obtains knowledge to be applied in a case study.
|
|
| Mode of Delivery |
: |
Face-to-Face |
|
| Prerequisites and Co-Prerequisites |
: |
None |
|
| Recommended Optional Programme Components |
: |
None |
|
| Aim(s) of Course |
: |
This course will deal with meta-heuristic techniques and their uses in industrial engineering. At the end of the course the obtained knowledge will be applied in a case study. |
|
| Course Contents |
: |
Introduction to Modern Heuristics, Artificial Neural Networks, Simulated Annealing, Tabu Search, Genetic Algorithm, Differential Evolution, Ant Colony Optimization, Particle Swarm Optimization, Fuzzy Logic |
|
| Language of Instruction |
: |
English |
|
| Work Place |
: |
Classroom, Laboratory |
|
|
Course Outline /Schedule (Weekly) Planned Learning Activities |
| Week | Subject | Student's Preliminary Work | Learning Activities and Teaching Methods |
|
1 |
Introduction to Modern Heuristics-I |
Reading lecture notes and references about the subject |
Lecture, laboratory |
|
2 |
Introduction to Modern Heuristics-II |
Reading lecture notes and references about the subject |
Lecture, laboratory |
|
3 |
Artificial Neural Networks |
Reading lecture notes and references about the subject |
Lecture, laboratory |
|
4 |
Simulated Annealing-I |
Reading lecture notes and references about the subject |
Lecture, laboratory |
|
5 |
Simulated Annealing-II |
Reading lecture notes and references about the subject |
Lecture, laboratory |
|
6 |
Tabu Search-I |
Reading lecture notes and references about the subject |
Lecture, laboratory |
|
7 |
Tabu Search-II |
Reading lecture notes and references about the subject |
Lecture, laboratory |
|
8 |
Midterm |
Study for exam |
Written Exam |
|
9 |
Genetic Algorithm-I |
Reading lecture notes and references about the subject |
Lecture, laboratory |
|
10 |
Genetic Algorithm-II |
Reading lecture notes and references about the subject |
Lecture, laboratory |
|
11 |
Differential Evolution-1 |
Reading lecture notes and references about the subject |
Lecture, laboratory |
|
12 |
Differential Evolution-2 |
Reading lecture notes and references about the subject |
Lecture, laboratory |
|
13 |
Ant Colony Optimization |
Reading lecture notes and references about the subject |
Lecture, laboratory |
|
14 |
Particle Swarm Optimization |
Reading lecture notes and references about the subject |
Lecture, laboratory |
|
15 |
Fuzzy Logic |
Reading lecture notes and references about the subject |
Lecture, laboratory |
|
16/17 |
Final Exam |
Study for exam |
Written Exam |
|
|
|
Required Course Resources |
| Resource Type | Resource Name |
| Recommended Course Material(s) |
 Cura T., Modern Heuristic Techniques and Applications, 2008
Reeves C. R., Modern Heuristic Techniques for Combinatorial Problems,1995.
|
| |
| Required Course Material(s) | |
|
|
|
Assessment Methods and Assessment Criteria |
|
Semester/Year Assessments |
Number |
Contribution Percentage |
|
Mid-term Exams (Written, Oral, etc.) |
1 |
80 |
|
Homeworks/Projects/Others |
2 |
20 |
|
Total |
100 |
|
Rate of Semester/Year Assessments to Success |
40 |
| |
|
Final Assessments
|
100 |
|
Rate of Final Assessments to Success
|
60 |
|
Total |
100 |
|
|
| Contribution of the Course to Key Learning Outcomes |
| # | Key Learning Outcome | Contribution* |
|
1 |
Understand, interpret and apply knowledge in his/her field domain both in-depth and in-breadth by doing scientific research in industrial engineering. |
4 |
|
2 |
Acquire comprehensive knowledge about methods and tools of industrial engineering and their limitations. |
5 |
|
3 |
Work in multi-disciplinary teams and take a leading role and responsibility. |
3 |
|
4 |
Identify, gather and use necessary information and data. |
4 |
|
5 |
Complete and apply the knowledge by using scarce and limited resources in a scientific way and integrate the knowledge into various disciplines. |
4 |
|
6 |
Keep up with the recent changes and applications in the field of Industrial Engineering and analyze these innovations when necessary. |
5 |
|
7 |
Work in multi-disciplinary teams, take a leading role and responsibility and develop solutions for complex problems. |
3 |
|
8 |
Analyze Industrial Engineering problems, develop innovative methods to solve the problems. |
5 |
|
9 |
Have the ability to propose new and/or original ideas and methods in developing innovative solutions for designing systems, components or processes. |
3 |
|
10 |
Design and perform analytical modeling and experimental research and analyze/solve complex matters emerged in this process. |
4 |
|
11 |
Follow, study and learn new and developing applications of industrial engineering. |
5 |
|
12 |
Use a foreign language in verbal and written communication at least B2 level of European Language Portfolio. |
5 |
|
13 |
Present his/her research findings systematically and clearly in oral and written forms in national and international platforms. |
4 |
|
14 |
Understand social and environmental implications of engineering practice. |
4 |
|
15 |
Consider social, scientific and ethical values in the process of data collection, interpretation and announcement of the findings. |
4 |
| * Contribution levels are between 0 (not) and 5 (maximum). |
|
|
| Student Workload - ECTS |
| Works | Number | Time (Hour) | Total Workload (Hour) |
| Course Related Works |
|
Class Time (Exam weeks are excluded) |
14 |
3 |
42 |
|
Out of Class Study (Preliminary Work, Practice) |
14 |
2 |
28 |
| Assesment Related Works |
|
Homeworks, Projects, Others |
2 |
15 |
30 |
|
Mid-term Exams (Written, Oral, etc.) |
1 |
20 |
20 |
|
Final Exam |
1 |
20 |
20 |
|
Total Workload: | 140 |
| Total Workload / 25 (h): | 5.6 |
| ECTS Credit: | 6 |
|
|
|