COURSE UNIT TITLE

: APPLICATION OF OPTIMIZATION TECHNIQUES IN MARINE TRANSPORTATION ENGINEERING

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
MTE 5033 APPLICATION OF OPTIMIZATION TECHNIQUES IN MARINE TRANSPORTATION ENGINEERING ELECTIVE 3 0 0 8

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

ASSOCIATE PROFESSOR BURAK KÖSEOĞLU

Offered to

Marine Transportation Systems Engineering
Marine Transportation Systems Engineering
MARINE TRANSPORTATION SYSTEMS ENGINEERING

Course Objective

Having knowledge about classical and modern optimization techniques, an ability to apply them to engineering problems, the importance of optimization theory , scope, and gain a broad understanding on the current situation. Optimization theory with examples and applications. Introduction to modern optimization methods . Understand the different type of optimization and intelligent techniques.

Learning Outcomes of the Course Unit

1   To understand the importance of optimization as a mathematical tool of engineering
2   An ability to apply optimization techniques to the classic real engineering problems
3   Having knowledge about modern optimization methods
4   An ability to use a variety of software including optimization tools
5   Having knowledge about the limits and applicability of certain optimization techniques

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Overview of the course , presentation of the general framework of the topics, to define the basic terminology for the course.
2 Introduction to Optimization , Basic Concepts
3 Classification of optimization problems and methods
4 Classical optimization
5 Introduction to Modern Methods : History, Current Status, Needs
6 Intelligent optimization
7 Midterm Exam
8 Fuzzy Logic Applications
9 Genetic Algorithm Applications
10 Artificial Neural Networks Applications
11 Ant Colony Algorithm Applications
12 Annealing Algorithm Applications
13 Swarm Optimization
14 Project Evaluation
15 General evaluation
16 Final Exam

Recomended or Required Reading

- S.S Rao., Optimization: Theory and Practices, New Age Int. (P) Ltd. Publishers, New Delhi.
- Chong, E.K.P.and Zak, S. H.. An Introduction to Optimization, John Wiley & Sons, N.Y.

Planned Learning Activities and Teaching Methods

Weekly draw a general framework on the subject .
Materials provided by the basic textbook and benefiting from other sources that make up a presentation with lectures .
Supported by case studies of the course topics .
Analysis of a given case - study.
Effective teaching and learning strategies.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 ASG ASSIGNMENT
3 FIN FINAL EXAM
4 FCG FINAL COURSE GRADE MTE * 0.30 +ASG * 0.20 +FIN * 0.50
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.30 + ASG * 0.20 + RST * 0.50


Further Notes About Assessment Methods

None

Assessment Criteria

Knowledge about optimization techniques at graduate level, having skills and competencies, research, analysis, interpretation, verbal and written expression, innovation, creativity and entrepreneurial skills and competencies will be evaluated.

Language of Instruction

English

Course Policies and Rules

The active participation of all students is essential

Contact Details for the Lecturer(s)

0232 301 88 19

Office Hours

09.00 - 17.00

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 3 42
Preparations before/after weekly lectures 14 6 84
Preparation for midterm exam 1 12 12
Preparation for final exam 1 20 20
Preparing assignments 1 6 6
Preparing presentations 1 30 30
Final 1 3 3
Midterm 1 2 2
TOTAL WORKLOAD (hours) 199

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11
LO.13554
LO.254
LO.35
LO.435
LO.55