Course Detail
Course Description
| Course | Code | Semester | T+P (Hour) | Credit | ECTS |
|---|---|---|---|---|---|
| INTRODUCTION to MODELLING and OPTIMIZATION | - | Spring Semester | 3+2 | 4 | 8 |
| Course Program |
| Prerequisites Courses | |
| Recommended Elective Courses |
| Language of Course | English |
| Course Level | First Cycle (Bachelor's Degree) |
| Course Type | Required |
| Course Coordinator | Assoc.Prof. Yasin GÖÇGÜN |
| Name of Lecturer(s) | Prof.Dr. Hakan TOZAN, Assist.Prof. İrem DÜZDAR ARGUN |
| 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 | 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 engineering problems; ability to select and apply proper analysis and modeling methods for this purpose. | X | |||||
| 3 | Ability to design a complex 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 engineering practice; ability to employ information technologies effectively. | X | |||||
| 5 | Ability to design and conduct experiments, gather data, analyze and interpret results for investigating complex engineering problems or discipline specific research questions. | X | |||||
| 6 | Ability to work efficiently in intra-disciplinary and multi-disciplinary teams; ability to work individually. | X | |||||
| 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. | X | |||||
| 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 | Knowledge on behavior according ethical principles, professional and ethical responsibility and standards used in engineering practices. | ||||||
| 10 | Knowledge about business life practices such as project management, risk management, and change management; awareness in entrepreneurship, innovation; knowledge about sustainable development. | X | |||||
| 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. | X | |||||
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 | - | Spring Semester | 3+2 | 4 | 8 |
| Course Program |
| Prerequisites Courses | |
| Recommended Elective Courses |
| Language of Course | English |
| Course Level | First Cycle (Bachelor's Degree) |
| Course Type | Required |
| Course Coordinator | Assoc.Prof. Yasin GÖÇGÜN |
| Name of Lecturer(s) | Prof.Dr. Hakan TOZAN, Assist.Prof. İrem DÜZDAR ARGUN |
| 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 | 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 engineering problems; ability to select and apply proper analysis and modeling methods for this purpose. | X | |||||
| 3 | Ability to design a complex 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 engineering practice; ability to employ information technologies effectively. | X | |||||
| 5 | Ability to design and conduct experiments, gather data, analyze and interpret results for investigating complex engineering problems or discipline specific research questions. | X | |||||
| 6 | Ability to work efficiently in intra-disciplinary and multi-disciplinary teams; ability to work individually. | X | |||||
| 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. | X | |||||
| 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 | Knowledge on behavior according ethical principles, professional and ethical responsibility and standards used in engineering practices. | ||||||
| 10 | Knowledge about business life practices such as project management, risk management, and change management; awareness in entrepreneurship, innovation; knowledge about sustainable development. | X | |||||
| 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. | X | |||||
Assessment Methods
| Contribution Level | Absolute Evaluation | |
| Rate of Midterm Exam to Success | 30 | |
| Rate of Final Exam to Success | 70 | |
| Total | 100 | |