ECTS - Introduction to Optimization

Introduction to Optimization (MFGE412) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Introduction to Optimization MFGE412 3 0 0 3 5
Pre-requisite Course(s)
MATH276, MATH380
Course Language English
Course Type N/A
Course Level Bachelor’s Degree (First Cycle)
Mode of Delivery Face To Face
Learning and Teaching Strategies Lecture, Question and Answer, Drill and Practice.
Course Coordinator
Course Lecturer(s)
Course Assistants
Course Objectives Implementation of optimization methods using MATLAB.
Course Learning Outcomes The students who succeeded in this course;
  • Understand optimization concepts in the solution of engineering problems (problem formulation, mathematical modeling, search and solution methods).
  • Gain skills in application of optimization tools in the analysis and solution of the engineering problems, implementation of optimization methods using MATLAB.
  • Improve skills on programming
  • Gain knowledge in optimization techniques and applications
Course Content Introduction to optimization, graphical optimization, least squares regression, linear and non-linear programming, numerical techniques, unconstrained and constrained optimization, global optimization (genetic algorithm), applications.

Weekly Subjects and Releated Preparation Studies

Week Subjects Preparation
1 Introduction to optimization. Chapters 13
2 Numerical Techniques Chapter 13
3 Graphical Optimization Chapter 15
4 Curve fitting (linear) Chapter 17
5 Curve fitting (linearizing) Chapter 17
6 Linear Programming Chapter 15
7 Linear Programming Chapter 15
8 Nonlinear Programming Chapter 14
9 Constrained Optimization Chapter 15
10 Genetic Algorithm Lecture notes
11 Genetic Algorithm Lecture notes
12 MATLAB Applications Chapter 15
13 Applications on Engineering Problems Chapter 16 and private study
14 Applications on Engineering Problems Chapter 16 and private study
15 Final exam period All chapters
16 Final exam period All chapters


Course Book 1. Numerical Methods for Engineers, S.C. Chapra & R.P. Canale, 5th Edition, McGraw-Hill, 2006
Other Sources 2. Applied Optimization with MATLAB Programming, Wiley, by P. Venkataraman (2002).
3. Practical Optimization (Algorithms and Engineering Applications), (Springer) by Antoniou, Andreas and Lu, Wu-Sheng (2007).
4. Numerical Optimization (Springer) by Jorge Nocedal and Stephen Wright (2006)
5. Practical Mathematical Optimization: An Introduction to Basic Optimization Theory and Classical and New Gradient-based Algorithms (Applied Optimization) by Jan A. Snyman (2005)

Evaluation System

Requirements Number Percentage of Grade
Attendance/Participation 1 5
Laboratory 5 25
Application - -
Field Work - -
Special Course Internship - -
Quizzes/Studio Critics - -
Homework Assignments - -
Presentation - -
Project - -
Report - -
Seminar - -
Midterms Exams/Midterms Jury 2 40
Final Exam/Final Jury 1 30
Toplam 9 100
Percentage of Semester Work 70
Percentage of Final Work 30
Total 100

Course Category

Core Courses
Major Area Courses X
Supportive Courses
Media and Managment Skills Courses
Transferable Skill Courses

The Relation Between Course Learning Competencies and Program Qualifications

# Program Qualifications / Competencies Level of Contribution
1 2 3 4 5
1 An ability to apply knowledge in mathematics and basic sciences and computational skills to solve manufacturing engineering problems X
2 An ability to define and analyze issues related with manufacturing technologies X
3 An ability to develop a solution based approach and a model for an engineering problem and design and manage an experiment X
4 An ability to design a comprehensive manufacturing system based on creative utilization of fundamental engineering principles while fulfilling sustainability in environment and manufacturability and economic constraints X
5 An ability to chose and use modern technologies and engineering tools for manufacturing engineering applications X
6 An ability to utilize information technologies efficiently to acquire datum and analyze critically, articulate the outcome and make decision accordingly X
7 An ability to attain self-confidence and necessary organizational work skills to participate in multi-diciplinary and interdiciplinary teams as well as act individually X
8 An ability to attain efficient communication skills in Turkish and English both verbally and orally X
9 An ability to reach knowledge and to attain life-long learning and self-improvement skills, to follow recent advances in science and technology X
10 An awareness and responsibility about professional, legal, ethical and social issues in manufacturing engineering X
11 An awareness about solution focused project and risk management, enterpreneurship, innovative and sustainable development X
12 An understanding on the effects of engineering applications on health, social and legal aspects at universal and local level during decision making process X

ECTS/Workload Table

Activities Number Duration (Hours) Total Workload
Course Hours (Including Exam Week: 16 x Total Hours)
Laboratory 5 3 15
Special Course Internship
Field Work
Study Hours Out of Class 16 3 48
Presentation/Seminar Prepration
Homework Assignments 5 4 20
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury 2 5 10
Prepration of Final Exams/Final Jury 1 5 5
Total Workload 98