COURSE INTRODUCTION AND APPLICATION INFORMATION


Course Name
Heuristics in Optimization
Code
Semester
Theory
(hour/week)
Application/Lab
(hour/week)
Local Credits
ECTS
IE 358
Fall/Spring
3
0
3
6
Prerequisites
 IE 251To succeed (To get a grade of at least DD)
Course Language
English
Course Type
Elective
Course Level
First Cycle
Mode of Delivery -
Teaching Methods and Techniques of the Course Lecturing / Presentation
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;
  • Will be able to list the basic heuristic methods for optimization
  • Will be able to compare and contrast these methods with classical optimization methods
  • Will be able to list basic meta-heuristic methods for optimization
  • Will be able to adapt these heuristic methods especially to Industrial Systems Engineering problems
  • Will be able to improve these heuristic methods adapted to Industrial Systems Engineering problems
  • Will be able to implement Improve these heuristic methods adapted to Industrial Systems Engineering problems
Course Description This course introduces the concept of heuristics to students who 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.
Related Sustainable Development Goals

 



Course Category

Core Courses
Major Area Courses
X
Supportive Courses
Media and Managment Skills Courses
Transferable Skill Courses

 

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week Subjects Required Materials
1 Reminder for Optimization Reading: Textbook (Michalewicz) introduction Ch 1
2 Introduction to complexity and heuristics Lecture notes/slides provided
3 Simulated Annealing Lecture notes/ slides provided, Reading: Related textbook (Michalewicz) chapter 5 and Handbook of Metaheuristics Ch 8
4 Particle Swarm Optimization Lecture notes/slides provided
5 Genetic Algorithms and Evolutionary Strategies 1 Lecture notes/slides provided, Reading: Related textbook (Michalewicz) chapter 5 and Handbook of Metaheuristics Ch 3
6 Genetic Algorithms and Evolutionary Strategies 1 Lecture notes/slides provided, Reading: Related textbook (Michalewicz) chapter 5 and Handbook of Metaheuristics Ch 3
7 MIDTERM
8 Ant Colony Optimization Lecture notes/slides provided, Reading: Handbook of Metaheuristics Ch 9
9 Tabu Search Lecture notes/slides provided, Reading: Related textbook (Michalewicz) chapter 5 and Handbook of Metaheuristics Ch 2
10 Tabu Search Lecture notes/slides provided, Reading: Related textbook (Michalewicz) chapter 5 and Handbook of Metaheuristics Ch 2
11 GRASP Lecture notes/slides provided Handbook of Metaheuristics Ch 8
12 Scatter Search Lecture notes/slides provided Handbook of Metaheuristics Ch 1
13 Local Search 1 Lecture notes/slides provided Handbook of Metaheuristics Ch 11
14 Local Search 2 Neighbourhoods, VNS Lecture notes/slides provided Reading: Handbook of MetaheuristicsCh 6
15 Review of Final Lecture notes/slides provided
16 Review of the Semester  
Course Notes/Textbooks Textbook:Zbigniew Michalewicz, David B. Fogel “How to Solve It: Modern Heuristics
Suggested Readings/Materials "Handbook of Metaheuristics" edt by: Glover F.,, Kochenberger G.A., Kluwer, 2003 and Lecture PowerPoint slides

 

EVALUATION SYSTEM

Semester Activities Number Weigthing
Participation
1
5
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
5
20
Presentation / Jury
Project
1
35
Seminar / Workshop
Oral Exam
Midterm
1
20
Final Exam
1
20
Total

Weighting of Semester Activities on the Final Grade
8
80
Weighting of End-of-Semester Activities on the Final Grade
1
20
Total

ECTS / WORKLOAD TABLE

Semester Activities Number Duration (Hours) Workload
Course Hours
(Including exam week: 16 x total hours)
16
3
48
Laboratory / Application Hours
(Including exam week: 16 x total hours)
16
Study Hours Out of Class
14
5
70
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
5
3
Presentation / Jury
Project
1
17
Seminar / Workshop
Oral Exam
Midterms
1
10
Final Exams
1
20
    Total
180

 

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#
Program Competencies/Outcomes
* Contribution Level
1
2
3
4
5
1

To have adequate knowledge in Mathematics, Science and Industrial Engineering; to be able to use theoretical and applied information in these areas to model and solve Industrial Engineering problems.

X
2

To be able to identify, formulate and solve complex Industrial Engineering problems by using state-of-the-art methods, techniques and equipment; to be able to select and apply proper analysis and modeling methods for this purpose.

X
3

To be able to analyze a complex system, process, device or product, and to design with realistic limitations to meet the requirements using modern design techniques. 

X
4

To be able to choose and use the required modern techniques and tools for Industrial Engineering applications; to be able to use information technologies efficiently.

X
5

To be able to design and do simulation and/or experiment, collect and analyze data and interpret the results for investigating Industrial Engineering problems and Industrial Engineering related research areas.

X
6

To be able to work efficiently in Industrial Engineering disciplinary and multidisciplinary teams; to be able to work individually.

X
7

To be able to communicate effectively in Turkish, both orally and in writing; to be able to author and comprehend written reports, to be able to prepare design and implementation reports, to present effectively; to be able to give and receive clear and comprehensible instructions

8

To have knowledge about contemporary issues and the global and societal effects of Industrial Engineering practices on health, environment, and safety; to be aware of the legal consequences of Industrial Engineering solutions.

9

To be aware of professional and ethical responsibility; to have knowledge of the standards used in Industrial Engineering practice.

10

To have knowledge about business life practices such as project management, risk management, and change management; to be aware of entrepreneurship and innovation; to have knowledge about sustainable development.

11

To be able to collect data in the area of Industrial Engineering; to be able to communicate with colleagues in a foreign language.

12

To be able to speak a second foreign at a medium level of fluency efficiently.

13

To recognize the need for lifelong learning; to be able to access information, to be able to stay current with developments in science and technology; to be able to relate the knowledge accumulated throughout the human history to Industrial Engineering.

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest