ISM5254 Applied Optimization TechniquesBahçeşehir UniversityDegree Programs ENGINEERING MANAGEMENT (TURKISH, NON-THESIS)General Information For StudentsDiploma SupplementErasmus Policy StatementNational Qualifications
Master TR-NQF-HE: Level 7 QF-EHEA: Second Cycle EQF-LLL: Level 7

Course Introduction and Application Information

Course Code Course Name Semester Theoretical Practical Credit ECTS
ISM5254 Applied Optimization Techniques Spring 3 0 3 8
This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester.

Basic information

Language of instruction: Turkish
Type of course: Departmental Elective
Course Level:
Mode of Delivery: Face to face
Course Coordinator : Assoc. Prof. İBRAHİM MUTER
Recommended Optional Program Components: None
Course Objectives: The main aim of this course is to give the students an overview of important areas where optimization problems often are considered, and an overview of some important practical techniques for their solution. Another purpose of this course is to provide insights into such problem areas from both an application and theoretical perspective, including the analysis of an optimization model and suitable choices of solution approaches.

Learning Outcomes

The students who have succeeded in this course;
- understand the main principles behind the modeling of optimization problems
- have a clear overview of the most important classes of optimization problems
- understand at least one basic solution algorithm to solve each optimization problem class
- understand the logic behind the optimization solvers and use them

Course Content

1. Introduction
2. Linear Programming (LP) Modeling
3. LP Solution Algorithm I
4. LP Solution Algorithm II
5. Solvers
6. Network Models and Applications
7. Network Models and Applications
8. Midterm
9. Integer Programming and Applications
10. Integer Programming and Applications
11. Nonlinear Programming and Applications
12. Software Applications
13. Software Applications
14. Presentations
15. Presentations

Weekly Detailed Course Contents

Week Subject Related Preparation
1) Introduction
2) Linear Programming (LP) Modeling
3) LP Solution Algorithms
4) LP Solution Algorithms
5) Solvers
6) Network Models and Applications
7) Network Models and Applications
8) Midterm
9) Integer Programming and Applications
10) Integer Programming and Applications
11) Nonlinear Programming and Applications
12) Software Applications
13) Software Applications
14) Presentations


Course Notes / Textbooks: Operations Research: An Introduction, Hamdy A. Taha, Prentice Hall, 9th Edition
References: NA

Evaluation System

Semester Requirements Number of Activities Level of Contribution
Homework Assignments 2 % 10
Presentation 1 % 10
Project 1 % 20
Midterms 1 % 20
Final 1 % 40
Total % 100
Total % 100

ECTS / Workload Table

Activities Number of Activities Duration (Hours) Workload
Course Hours 12 3 36
Study Hours Out of Class 2 32 64
Presentations / Seminar 1 15 15
Project 1 30 30
Homework Assignments 2 20 40
Midterms 1 3 3
Final 1 3 3
Total Workload 191

Contribution of Learning Outcomes to Programme Outcomes

No Effect 1 Lowest 2 Low 3 Average 4 High 5 Highest
Program Outcomes Level of Contribution
1) To be process oriented
2) To integrate technical specifications with management functions 3
3) To improve management skills by taking into account the economic, social and environmental conditions
4) To launch fast and agile decision-making process
5) To lead disciplinary and multi-disciplinary teams, to develop solution approaches in complex situations, to work individually and to take responsibility 3
6) To have conscious of professional and ethical responsibility
7) To be aware of economic and legal implications of engineering solutions
8) To have ability of selection and efficient use of modern techniques, equipments and information technologies for engineering management 3
9) To have verbal and oral effective communication skills by using visual methods in Turkish and English
10) To have conscious of necessity to lifelong learning 2
11) To be aware of entrepreneurship, sustainability and innovation
12) To be capable of designing and conducting experiments and collecting data, analyzing and interpreting results 4