FACULTY OF ENGINEERING

Department of Computer Engineering

IE 357 | Course Introduction and Application Information

Course Name
Special Topics in Optimization
Code
Semester
Theory
(hour/week)
Application/Lab
(hour/week)
Local Credits
ECTS
IE 357
Fall/Spring
3
0
3
6

Prerequisites
  IE 252 To succeed (To get a grade of at least DD)
Course Language
English
Course Type
Service Course
Course Level
First Cycle
Mode of Delivery -
Teaching Methods and Techniques of the Course Lecture / Presentation
Course Coordinator
Course Lecturer(s)
Assistant(s) -
Course Objectives To teach students optimization methods and modelling techniques not taught in compulsory courses
Learning Outcomes The students who succeeded in this course;
  • Will be able to describe the limitations of classical optimization methods
  • Will be able to model production management and industrial systems engineering field problems using these methods
  • Will be able to model production management and industrial systems engineering field problems using dynamic programming
  • Will be able to model production management and industrial systems engineering field problems using hybrid optimization methods
  • Will be able to solve these production management and industrial systems engineering field problems models' using appropriate software
Course Description In this course, students will have the chance to learn certain optimization subjects, methods and models which are not covered in compulsory courses. At the end students will also have the chance to learn applications of these models and methods.

 



Course Category

Core Courses
Major Area Courses
Supportive Courses
Media and Management Skills Courses
Transferable Skill Courses

 

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week Subjects Related Preparation
1 Fundamentals of Optimization Review Lecture Notes
2 Large Scale Optimization Solution methods LP Relaxation Lecture Notes
3 Large Scale Optimization Solution methods LP Relaxation Lecture Notes
4 Large Scale Optimization Solution methods Dantzig Wolfe Lecture Notes
5 Large Scale Optimization Solution methods B&B Lecture Notes
6 Large Scale Optimization Solution methods B&B Lecture Notes
7 Large Scale Optimization Solution methods Lagrangian Relaxation Lecture Notes
8 Large Scale Optimization Solution methods Lagrangian Relaxation Lecture Notes
9 Midterm
10 Large Scale Optimization Solution methods Cutting Plane Lecture Notes
11 Large Scale Optimization Solution methods Bender Decomposition Lecture Notes
12 Dynamic Programming Lecture Notes
13 Dynamic Programming Lecture Notes
14 Dynamic Programming Lecture Notes
15 Review and Presentations Lecture Notes
16 Review of the Semester  

 

Course Notes/Textbooks
Suggested Readings/Materials

 

EVALUATION SYSTEM

Semester Activities Number Weigthing
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
Presentation / Jury
Project
Seminar / Workshop
Oral Exams
Midterm
2
60
Final Exam
1
40
Total

Weighting of Semester Activities on the Final Grade
60
Weighting of End-of-Semester Activities on the Final Grade
40
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
14
4
56
Field Work
0
Quizzes / Studio Critiques
0
Portfolio
0
Homework / Assignments
0
Presentation / Jury
0
Project
0
Seminar / Workshop
0
Oral Exam
0
Midterms
2
22
44
Final Exam
1
32
32
    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 Computer Engineering; to be able to use theoretical and applied information in these areas on complex engineering problems.

2

To be able to identify, define, formulate, and solve complex Computer Engineering problems; to be able to select and apply proper analysis and modeling methods for this purpose.

3

To be able to design a complex system, process, device or product under realistic constraints and conditions, in such a way as to meet the requirements; to be able to apply modern design methods for this purpose.

4

To be able to devise, select, and use modern techniques and tools needed for analysis and solution of complex problems in Computer Engineering applications; to be able to use information technologies effectively.

5

To be able to design and conduct experiments, gather data, analyze and interpret results for investigating complex engineering problems or Computer Engineering research topics.

6

To be able to work efficiently in Computer Engineering disciplinary and multi-disciplinary teams; to be able to work individually.

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 global and social impact of Computer Engineering practices on health, environment, and safety; to have knowledge about contemporary issues as they pertain to engineering; to be aware of the legal ramifications of Computer Engineering solutions.

9

To be aware of ethical behavior, professional and ethical responsibility; to have knowledge about standards utilized in engineering applications.

10

To have knowledge about industrial practices such as project management, risk management, and change management; to have awareness of entrepreneurship and innovation; to have knowledge about sustainable development.

11

To be able to collect data in the area of Computer Engineering, and to be able to communicate with colleagues in a foreign language. ("European Language Portfolio Global Scale", Level B1)

12

To be able to speak a second foreign language 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 Computer Engineering.

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

 


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