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IE 509 | Course Introduction and Application Information
| Course Name |
Heuristics
|
|
Code
|
Semester
|
Theory
(hour/week) |
Application/Lab
(hour/week) |
Local Credits
|
ECTS
|
|
IE 509
|
Fall/Spring
|
3
|
0
|
3
|
7.5
|
| Prerequisites |
None
|
|||||
| Course Language |
English
|
|||||
| Course Type |
Elective
|
|||||
| Course Level |
Second Cycle
|
|||||
| Mode of Delivery | - | |||||
| Teaching Methods and Techniques of the Course | - | |||||
| National Occupation Classification | - | |||||
| Course Coordinator | ||||||
| Course Lecturer(s) | ||||||
| Assistant(s) | - | |||||
| Course Objectives | The purpose of this course is to fundamental concepts of heuristics in solving various optimization problems with emphasis on metaheuristics |
| Learning Outcomes |
The students who succeeded in this course;
|
| Course Description | This course introduces the concept of heuristics to the students who have already know about mathematical optimization. The topics include basic heuristic constructs (greedy, improvement, construction); meta heuristics such as simulated annealing, tabu search, genetic algorithms, ant algorithms and their hybrids. The basic material on the heuristic will be covered in regular lectures The students will be required to present a variety of application papers on different subjects related to the course. In addition, as a project assignment the students will design a heuristic, write a code of an appropriate algorithm for the problem and evaluate its performance. |
|
|
Core Courses | |
| Major Area Courses |
X
|
|
| Supportive Courses | ||
| Media and Management Skills Courses | ||
| Transferable Skill Courses |
WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES
| Week | Subjects | Related Preparation | Learning Outcome |
| 1 | Heuristics Basic Terminalogy | ||
| 2 | Complexity | ||
| 3 | Basic Search Procedures | ||
| 4 | Simulated Annealing | ||
| 5 | Tabu Search | ||
| 6 | Genetic Algorithms and Evolutionary Search | ||
| 7 | Genetic Algorithms and Evolutionary Search | ||
| 8 | Midterm | ||
| 9 | Particle Swarm Optimization | ||
| 10 | Ant Colony Optimization | ||
| 11 | Scatter Search | ||
| 12 | Local Search | ||
| 13 | Very Large Scale Neighborhood Search | ||
| 14 | Presentations | ||
| 15 | Presentations | ||
| 16 | Review of the Semester |
| Course Notes/Textbooks | E.G. Talbi. Metaheuristics: From Design to Implementation. Wiley 2009. F. Glover, G. Kochenberger. Handbook of Metaheuristics. Springer 2003. T. González. Handbook of Approximation Algorithms and Metaheuristics. Chapman & Hall 2007. F. Glover, M. Laguna. Tabu Search. Kluwer, 1997. M. Dorigo and T. Stützle. Ant Colony Optimization. MIT Press, Cambridge, MA, 2004.
|
| Suggested Readings/Materials | Related Research Papers |
EVALUATION SYSTEM
| Semester Activities | Number | Weigthing |
| Participation | ||
| Laboratory / Application | ||
| Field Work | ||
| Quizzes / Studio Critiques | ||
| Portfolio | ||
| Homework / Assignments |
2
|
15
|
| Presentation / Jury |
1
|
15
|
| Project |
1
|
50
|
| Seminar / Workshop | ||
| Oral Exams | ||
| Midterm |
1
|
20
|
| Final Exam |
-
|
|
| Total |
| Weighting of Semester Activities on the Final Grade |
100
|
|
| Weighting of End-of-Semester Activities on the Final Grade | ||
| 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
|
4
|
60
|
| Field Work |
0
|
||
| Quizzes / Studio Critiques |
0
|
||
| Portfolio |
0
|
||
| Homework / Assignments |
2
|
20
|
40
|
| Presentation / Jury |
1
|
17
|
17
|
| Project |
1
|
40
|
40
|
| Seminar / Workshop |
0
|
||
| Oral Exam |
0
|
||
| Midterms |
1
|
20
|
20
|
| Final Exam |
-
|
0
|
|
| Total |
225
|
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. |
-
|
-
|
-
|
-
|
X
|
|
| 2 |
To be able to identify, formulate and solve Industrial Engineering-related problems by using state-of-the-art methods, techniques and equipment. |
-
|
-
|
-
|
-
|
X
|
|
| 3 |
To be able to use techniques and tools for analyzing and designing industrial systems with a commitment to quality. |
-
|
-
|
X
|
-
|
-
|
|
| 4 |
To be able to conduct basic research and write and publish articles in related conferences and journals. |
-
|
-
|
X
|
-
|
-
|
|
| 5 |
To be able to carry out tests to measure the performance of industrial systems, analyze and interpret the subsequent results. |
-
|
X
|
-
|
-
|
-
|
|
| 6 |
To be able to manage decision-making processes in industrial systems. |
-
|
-
|
-
|
-
|
X
|
|
| 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. |
-
|
-
|
-
|
X
|
-
|
|
| 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. |
-
|
-
|
-
|
X
|
-
|
|
| 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. |
-
|
-
|
-
|
X
|
-
|
|
| 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. |
-
|
-
|
-
|
X
|
-
|
|
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest
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