Course Detail
Course Description
Course | Code | Semester | T+P (Hour) | Credit | ECTS |
---|
INTRODUCTION to MODELLING and OPTIMIZATION | COE3349050 | Summer Semester | 3+2 | 4 | 8 |
Prerequisites Courses | |
Recommended Elective Courses | |
Language of Course | English |
Course Level | First Cycle (Bachelor's Degree) |
Course Type | Elective |
Course Coordinator | Assoc.Prof. Yasin GÖÇGÜN |
Name of Lecturer(s) | Assoc.Prof. Yasin GÖÇGÜN |
Assistant(s) | |
Aim | The aim and objective of this course are to teach. how to formulate and analyze mathematical models (with selected real-world applications)and, mathematical tools to handle linear programming and network problems (the simplex method, duality, sensitivity analysis, and related topics, network models, and project scheduling). |
Course Content | This course contains; Introduction to Model Building,Basic Linear Algebra,Introduction to Linear Programming,Convex Sets and Functions, Extreme Points and Optimality, Graphical Solution,Graphical Sensitivity Analysis and Computer Based Solutions,Simplex Algorithm
,Simplex Algorithm: Artificial Starting Solutions,Simplex Algorithm: Artificial Starting Solutions and Special Cases in Simplex,Revised Simplex ,Special Simplex Implementations: Karus-Kuhn-Tucker Optimality Conditions,Duality and Sensitivity,Duality and Sensitivity: Dual Simplex,Transportation and Assignment Problems-1,Transportation and Assignment Problems-2. |
Dersin Öğrenme Kazanımları | Teaching Methods | Assessment Methods |
Students define modeling concepts. | 12, 13, 14, 16, 6, 8, 9 | A, E, G, H |
Students analyze mathematical models. | 12, 13, 14, 16, 6, 8, 9 | A, E, H |
Students formulate problems using linear programming. | 12, 14, 16, 21, 6, 8, 9 | A, G |
Students implement the Simplex algorithm. | 12, 14, 16, 8, 9 | G |
Students define duality and sensitivity analysis. | 12, 14, 16, 9 | A |
Students solve transportation and assignment models. | 12, 14, 16, 6, 9 | A |
Teaching Methods: | 12: Problem Solving Method, 13: Case Study Method, 14: Self Study Method, 16: Question - Answer Technique, 21: Simulation Technique, 6: Experiential Learning, 8: Flipped Classroom Learning, 9: Lecture Method |
Assessment Methods: | A: Traditional Written Exam, E: Homework, G: Quiz, H: Performance Task |
Course Outline
Order | Subjects | Preliminary Work |
---|
1 | Introduction to Model Building | Examining the course textbook |
2 | Basic Linear Algebra | Examining the course textbook |
3 | Introduction to Linear Programming | Examining the course textbook |
4 | Convex Sets and Functions, Extreme Points and Optimality, Graphical Solution | Examining the course textbook |
5 | Graphical Sensitivity Analysis and Computer Based Solutions | Examining the course textbook |
6 | Simplex Algorithm
| Examining the course textbook |
7 | Simplex Algorithm: Artificial Starting Solutions | Examining the course textbook |
8 | Simplex Algorithm: Artificial Starting Solutions and Special Cases in Simplex | Examining the course textbook |
9 | Revised Simplex | Examining the course textbook |
10 | Special Simplex Implementations: Karus-Kuhn-Tucker Optimality Conditions | Examining the course textbook |
11 | Duality and Sensitivity | Examining the course textbook |
12 | Duality and Sensitivity: Dual Simplex | Examining the course textbook |
13 | Transportation and Assignment Problems-1 | Examining the course textbook |
14 | Transportation and Assignment Problems-2 | Examining the course textbook |
Resources |
Taha, Hamdy A., Operations Research, 8th edition, 2007. ISBN: 0131360140 |
Winston, Wayne L., Operations Research: Applications and Algorithms, 4th edition, 2003. ISBN-13: 978-0534380588 (Course notes and other material may be provided by the instructor) |
Course Contribution to Program Qualifications
Course Contribution to Program Qualifications |
No | Program Qualification | Contribution Level |
1 | 2 | 3 | 4 | 5 |
1 | 1. An ability to apply knowledge of mathematics, science, and engineering | | | | | X |
2 | 2. An ability to identify, formulate, and solve engineering problems | | | | | X |
3 | 3. An ability to design a system, component, or process to meet desired needs within realistic constraints such as economic, environmental, social, political, ethical, health and safety, manufacturability, and sustainability | | | | X | |
4 | 4. An ability to use the techniques, skills, and modern engineering tools necessary for engineering practice | | | | | X |
5 | 5. An ability to design and conduct experiments, as well as to analyze and interpret data | | | | X | |
6 | 6. An ability to function on multidisciplinary teams | | | X | | |
7 | 7. An ability to communicate effectively | | X | | | |
8 | 8. A recognition of the need for, and an ability to engage in life-long learning | | | X | | |
9 | 9. An understanding of professional and ethical responsibility | | X | | | |
10 | 10. A knowledge of contemporary issues | | | | | |
11 | 11. The broad education necessary to understand the impact of engineering solutions in a global, economic, environmental, and societal context | | | | | |
Assessment Methods
Contribution Level | Absolute Evaluation |
Rate of Midterm Exam to Success | | 30 |
Rate of Final Exam to Success | | 70 |
Total | | 100 |
ECTS / Workload Table |
Activities | Number of | Duration(Hour) | Total Workload(Hour) |
Course Hours | 14 | 3 | 42 |
Guided Problem Solving | 14 | 2 | 28 |
Resolution of Homework Problems and Submission as a Report | 14 | 2 | 28 |
Term Project | 0 | 0 | 0 |
Presentation of Project / Seminar | 0 | 0 | 0 |
Quiz | 4 | 15 | 60 |
Midterm Exam | 1 | 30 | 30 |
General Exam | 1 | 40 | 40 |
Performance Task, Maintenance Plan | 0 | 0 | 0 |
Total Workload(Hour) | 228 |
Dersin AKTS Kredisi = Toplam İş Yükü (Saat)/30*=(228/30) | 8 |
ECTS of the course: 30 hours of work is counted as 1 ECTS credit. |
Detail Informations of the Course
Course Description
Course | Code | Semester | T+P (Hour) | Credit | ECTS |
---|
INTRODUCTION to MODELLING and OPTIMIZATION | COE3349050 | Summer Semester | 3+2 | 4 | 8 |
Prerequisites Courses | |
Recommended Elective Courses | |
Language of Course | English |
Course Level | First Cycle (Bachelor's Degree) |
Course Type | Elective |
Course Coordinator | Assoc.Prof. Yasin GÖÇGÜN |
Name of Lecturer(s) | Assoc.Prof. Yasin GÖÇGÜN |
Assistant(s) | |
Aim | The aim and objective of this course are to teach. how to formulate and analyze mathematical models (with selected real-world applications)and, mathematical tools to handle linear programming and network problems (the simplex method, duality, sensitivity analysis, and related topics, network models, and project scheduling). |
Course Content | This course contains; Introduction to Model Building,Basic Linear Algebra,Introduction to Linear Programming,Convex Sets and Functions, Extreme Points and Optimality, Graphical Solution,Graphical Sensitivity Analysis and Computer Based Solutions,Simplex Algorithm
,Simplex Algorithm: Artificial Starting Solutions,Simplex Algorithm: Artificial Starting Solutions and Special Cases in Simplex,Revised Simplex ,Special Simplex Implementations: Karus-Kuhn-Tucker Optimality Conditions,Duality and Sensitivity,Duality and Sensitivity: Dual Simplex,Transportation and Assignment Problems-1,Transportation and Assignment Problems-2. |
Dersin Öğrenme Kazanımları | Teaching Methods | Assessment Methods |
Students define modeling concepts. | 12, 13, 14, 16, 6, 8, 9 | A, E, G, H |
Students analyze mathematical models. | 12, 13, 14, 16, 6, 8, 9 | A, E, H |
Students formulate problems using linear programming. | 12, 14, 16, 21, 6, 8, 9 | A, G |
Students implement the Simplex algorithm. | 12, 14, 16, 8, 9 | G |
Students define duality and sensitivity analysis. | 12, 14, 16, 9 | A |
Students solve transportation and assignment models. | 12, 14, 16, 6, 9 | A |
Teaching Methods: | 12: Problem Solving Method, 13: Case Study Method, 14: Self Study Method, 16: Question - Answer Technique, 21: Simulation Technique, 6: Experiential Learning, 8: Flipped Classroom Learning, 9: Lecture Method |
Assessment Methods: | A: Traditional Written Exam, E: Homework, G: Quiz, H: Performance Task |
Course Outline
Order | Subjects | Preliminary Work |
---|
1 | Introduction to Model Building | Examining the course textbook |
2 | Basic Linear Algebra | Examining the course textbook |
3 | Introduction to Linear Programming | Examining the course textbook |
4 | Convex Sets and Functions, Extreme Points and Optimality, Graphical Solution | Examining the course textbook |
5 | Graphical Sensitivity Analysis and Computer Based Solutions | Examining the course textbook |
6 | Simplex Algorithm
| Examining the course textbook |
7 | Simplex Algorithm: Artificial Starting Solutions | Examining the course textbook |
8 | Simplex Algorithm: Artificial Starting Solutions and Special Cases in Simplex | Examining the course textbook |
9 | Revised Simplex | Examining the course textbook |
10 | Special Simplex Implementations: Karus-Kuhn-Tucker Optimality Conditions | Examining the course textbook |
11 | Duality and Sensitivity | Examining the course textbook |
12 | Duality and Sensitivity: Dual Simplex | Examining the course textbook |
13 | Transportation and Assignment Problems-1 | Examining the course textbook |
14 | Transportation and Assignment Problems-2 | Examining the course textbook |
Resources |
Taha, Hamdy A., Operations Research, 8th edition, 2007. ISBN: 0131360140 |
Winston, Wayne L., Operations Research: Applications and Algorithms, 4th edition, 2003. ISBN-13: 978-0534380588 (Course notes and other material may be provided by the instructor) |
Course Contribution to Program Qualifications
Course Contribution to Program Qualifications |
No | Program Qualification | Contribution Level |
1 | 2 | 3 | 4 | 5 |
1 | 1. An ability to apply knowledge of mathematics, science, and engineering | | | | | X |
2 | 2. An ability to identify, formulate, and solve engineering problems | | | | | X |
3 | 3. An ability to design a system, component, or process to meet desired needs within realistic constraints such as economic, environmental, social, political, ethical, health and safety, manufacturability, and sustainability | | | | X | |
4 | 4. An ability to use the techniques, skills, and modern engineering tools necessary for engineering practice | | | | | X |
5 | 5. An ability to design and conduct experiments, as well as to analyze and interpret data | | | | X | |
6 | 6. An ability to function on multidisciplinary teams | | | X | | |
7 | 7. An ability to communicate effectively | | X | | | |
8 | 8. A recognition of the need for, and an ability to engage in life-long learning | | | X | | |
9 | 9. An understanding of professional and ethical responsibility | | X | | | |
10 | 10. A knowledge of contemporary issues | | | | | |
11 | 11. The broad education necessary to understand the impact of engineering solutions in a global, economic, environmental, and societal context | | | | | |
Assessment Methods
Contribution Level | Absolute Evaluation |
Rate of Midterm Exam to Success | | 30 |
Rate of Final Exam to Success | | 70 |
Total | | 100 |
Numerical Data
Ekleme Tarihi: 09/10/2023 - 10:50Son Güncelleme Tarihi: 09/10/2023 - 10:51
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