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 Area Elective 3 0 0 3 5
Pre-requisite Course(s)
N/A
Course Language English
Course Type Technical Elective Courses
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

Sources

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 Applies knowledge in mathematics, science, and computing to solve engineering problems related to manufacturing technologies. X
2 Analyzes and identifies problems specific to manufacturing technologies. X
3 Develops an approach to solve encountered engineering problems, and designs and conducts models and experiments. X
4 Designs a comprehensive manufacturing system (including method, product, or device development) based on the creative application of fundamental engineering principles, within constraints of economic viability, environmental sustainability, and manufacturability. X
5 Selects and uses modern techniques and engineering tools for manufacturing engineering applications. X
6 Effectively uses information technologies to collect and analyze data, think critically, interpret, and make sound decisions. X
7 Works effectively as a member of multidisciplinary and intra-disciplinary teams or individually; demonstrates the confidence and necessary organizational skills. X
8 Communicates effectively in both spoken and written Turkish and English. X
9 Engages in lifelong learning, accesses information, keeps up with the latest developments in science and technology, and continuously renews oneself. X
10 Demonstrates awareness and a sense of responsibility regarding professional, legal, ethical, and social issues in the field of Manufacturing Engineering. X
11 Effectively utilizes resources (personnel, equipment, and costs) to enhance national competitiveness and improve manufacturing industry productivity; conducts solution-oriented project and risk management; and demonstrates awareness of entrepreneurship, innovation, and sustainable development. X
12 Considers the health, environmental, social, and legal consequences of engineering practices at both global and local scales when making decisions. X

ECTS/Workload Table

Activities Number Duration (Hours) Total Workload
Course Hours (Including Exam Week: 16 x Total Hours)
Laboratory 5 3 15
Application
Special Course Internship
Field Work
Study Hours Out of Class 16 3 48
Presentation/Seminar Prepration
Project
Report
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