Description of Individual Course Units
Course Unit CodeCourse Unit TitleType of Course UnitYear of StudySemesterNumber of ECTS Credits
İST422OPTIMIZATION MODELS AND APPLICATIONS Elective485
Level of Course Unit
First Cycle
Objectives of the Course
The aim of this course is to provide students with the ability to model different types of optimization problems and to be able to code current methods that solve such problems.
Name of Lecturer(s)
Doç. Dr. Ali MERT
Learning Outcomes
1To be able to comprehend the theoretical background of optimization.
2To be able to recognize different types of optimization problems.
3To be able to model an optimization problem of the first time.
4Choose the method that can solve any optimization problem
5To be able to apply any method that solves an optimization problem.
6To be able to follow current publications about optimization.
7To be able to create the necessary codes to solve optimization problems.
Mode of Delivery
Face to Face
Prerequisites and co-requisities
No
Recommended Optional Programme Components
No
Course Contents
General information about the concept of optimization. Basic concepts of constrained nonlinear optimization problems. Methods for solving constrained nonlinear optimization problems. Different optimization problems and methods to solve them.
Weekly Detailed Course Contents
WeekTheoreticalPracticeLaboratory
1Mathematical foundations of optimization. Structure of optimization problem.Guided Problem Solving
2Basic theorems about constrained nonlinear optimization problems.Guided Problem Solving
3Zoutendijk method and applications.Guided Problem Solving
4Rosen method and applications.Guided Problem Solving
5Linear combinations method.Guided Problem Solving
6Fundamentals of quadratic programming.Guided Problem Solving
7Fundamentals of stochastic programming.Guided Problem Solving
8Midterm
9Basic information about C ++.Guided Problem Solving
10Coding of the Divide method.Guided Problem Solving
11Coding of the Golden Ratio method.Guided Problem Solving
12Coding of Fibonacci method.Guided Problem Solving
13Coding of Newton method.Guided Problem Solving
14Encoding the nearest neighborhood algorithmGuided Problem Solving
15Coding of Artificial Bee Colony algorithmGuided Problem Solving
16Final exam
Recommended or Required Reading
Planned Learning Activities and Teaching Methods
Assessment Methods and Criteria
Term (or Year) Learning ActivitiesQuantityWeight
SUM0
End Of Term (or Year) Learning ActivitiesQuantityWeight
SUM0
SUM0
Language of Instruction
Turkish
Work Placement(s)
No
Workload Calculation
ActivitiesNumberTime (hours)Total Work Load (hours)
TOTAL WORKLOAD (hours)0
Contribution of Learning Outcomes to Programme Outcomes
PO
1
PO
2
PO
3
PO
4
PO
5
PO
6
PO
7
PO
8
PO
9
PO
10
PO
11
PO
12
PO
13
PO
14
PO
15
PO
16
PO
17
PO
18
PO
19
PO
20
PO
21
PO
22
PO
23
PO
24
LO14                       
LO24                       
LO3  5       4             
LO4  5                     
LO5    4                   
LO6          53            
LO7    4     53            
* Contribution Level : 1 Very low 2 Low 3 Medium 4 High 5 Very High
 
Ege University, Bornova - İzmir / TURKEY • Phone: +90 232 311 10 10 • e-mail: intrec@mail.ege.edu.tr