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 Lecturer(s) |
|
| Course Objectives | Implementation of optimization methods using MATLAB. |
| Course Learning Outcomes |
The students who succeeded in this course;
|
| 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 | 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 |
| 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 | ||