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)
MATH276, MATH380
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 Adequate knowledge of mathematics, physical sciences and the subjects specific to engineering disciplines; the ability to apply theoretical and practical knowledge of these areas in the solution of complex engineering problems. X
2 The ability to define, formulate, and solve complex engineering problems; the ability to select and apply proper analysis and modeling methods for this purpose. X
3 The ability to design a complex system, process, device or product under realistic constraints and conditions in such a way as to meet the specific requirements; the ability to apply modern design methods for this purpose. X
4 The ability to select, and use modern techniques and tools needed to analyze and solve complex problems encountered in engineering practices; the ability to use information technologies effectively. X
5 The ability to design experiments, conduct experiments, gather data, and analyze and interpret results for investigating complex engineering problems or research areas specific to engineering disciplines.
6 The ability to work efficiently in inter-, intra-, and multi-disciplinary teams; the ability to work individually. X
7 Effective oral and written communication skills; The knowledge of, at least, one foreign language; the ability to write a report properly, understand previously written reports, prepare design and manufacturing reports, deliver influential presentations, give unequivocal instructions, and carry out the instructions properly. X
8 Recognition of the need for lifelong learning; the ability to access information, follow developments in science and technology, and adapt and excel oneself continuously.
9 Acting in conformity with the ethical principles; professional and ethical responsibility and knowledge of the standards employed in engineering applications.
10 Knowledge of business practices such as project management, risk management, and change management; awareness of entrepreneurship and innovation; knowledge of sustainable development.
11 Knowledge of the global and social effects of engineering practices on health, environment, and safety issues, and knowledge of the contemporary issues in engineering areas; awareness of the possible legal consequences of engineering practices.
12 Ability to work in the fields of both thermal and mechanical systems including the design and production steps of these systems.

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