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 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 X
Major Area Courses
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 in mathematics, science and engineering subjects pertaining to the relevant discipline; ability to use theoretical and applied knowledge in these areas in the solution of complex engineering problems. X
2 Ability to formulate, and solve complex mechatronics engineering problems; ability to select and apply proper analysis and modeling methods for this purpose. X
3 Ability to design a complex mechatronics engineering system, process, device or product under realistic constraints and conditions, in such a way as to meet the desired result; ability to apply modern design methods for this purpose. X
4 Ability to select and use modern techniques and tools needed for analyzing and solving complex problems encountered in mechatronics engineering and robot technology practices; ability to employ information technologies effectively.
5 Ability to design and conduct experiments, gather data, analyze and interpret results for investigating complex mechatronics engineering and robot technology problems or research questions.
6 Ability to work efficiently in intra-disciplinary and multi-disciplinary teams; ability to work individually.
7 Ability to communicate effectively, both orally and in writing; knowledge of a minimum of one foreign language; ability to write effective reports and comprehend written reports, prepare design and production reports, make effective presentations, and give and receive clear and intelligible instructions.
8 Awareness of the need for lifelong learning; ability to access information, to follow developments in science and technology, and to continue to educate him/herself
9 a-) Knowledge on behavior according to ethical principles, professional and ethical responsibility b-) Knowledge on standards used in engineering practices.
10 a-) Knowledge about business life practices such as project management, risk management, and change management b-) Awareness in entrepreneurship, innovation; knowledge about sustainable development.
11 Knowledge about the global and social effects of engineering practices on health, environment, and safety, and contemporary issues of the century reflected into the field of engineering; awareness of the legal consequences of engineering solutions.
12 Competency on defining, analyzing and surveying databases and other sources, proposing solutions based on research work and scientific results and communicate and publish numerical and conceptual solutions in the field of mechatronics engineering.
13 Consciousness on the environment and social responsibility, competencies on observation, improvement and modify and implementation of projects for the society and social relations and be an individual within the society in such a way that planning, improving or changing the norms with a criticism.

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