COURSE INTRODUCTION AND APPLICATION INFORMATION


Course Name
Discrete Optimization
Code
Semester
Theory
(hour/week)
Application/Lab
(hour/week)
Local Credits
ECTS
IE 510
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
Course Coordinator -
Course Lecturer(s) -
Assistant(s) -
Course Objectives Purpose of this course is to give the students an understanding and experience about discrete optimizaton problems, related concepts and exact and approximate solution techniques.
Learning Outcomes The students who succeeded in this course;
  • Be able to understand the nature of discrete optimization problems,
  • Be able to comprehend the concepts of optimality, relaxation, lower and upper bounds and cutting planes,
  • Be able to excel in discrete optimization methods including branch and bound, Lagrangian relaxation and dynamic programming,
  • Be able to develop simple approximation algorithms involving discrete optimization problems.
Course Description Topics of this course include optimality, relaxation, bounds, network flow problems, branch and bound, dynamic programming, cutting planes and approximation algorithms.
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 Introduction
2 Optimality, Relaxation and Bounds
3 Optimality, Relaxation and Bounds
4 Well-solved Cases: Network Flows, Shortest Path, Optimal Trees, Matching and Assignments
5 Well-solved Cases: Network Flows, Shortest Path, Optimal Trees, Matching and Assignments
6 Branch and Bound Methods
7 Branch and Bound Methods
8 Midterm exam
9 Cutting Plane Algorithms: Valid Inequalities, Theory and Practice
10 Cutting Plane Algorithms: Valid Inequalities, Theory and Practice
11 Cutting Plane Algorithms: Valid Inequalities, Theory and Practice
12 Dynamic Programming
13 Approximation Algorithms
14 Approximation Algorithms
15 General Review and Evaluation
16 General Review and Evaluation
Course Notes/Textbooks Instructor notes and lecture slides
Suggested Readings/Materials Integer Programming. Laurence A. Wolsey, Wiley, 1998.\nInteger and Combinatorial Optimization. Laurence A. Wolsey, George L. Nemhauser, Wiley, 1999.\nApplied Integer Programming: Modeling and Solution, Der-San Chen, Robert G. Batson, Yu Dang, Wiley, 2010.

 

EVALUATION SYSTEM

Semester Activities Number Weigthing
Participation
1
10
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
1
20
Presentation / Jury
Project
Seminar / Workshop
Oral Exam
Midterm
1
30
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
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
15
4
60
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
4
15
Presentation / Jury
Project
Seminar / Workshop
Oral Exam
Midterms
1
27
Final Exams
1
30
    Total
225

 

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#
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