| Course Name | Art of Mathematical Modelling |
| Code | Semester | Theory (hour/week) | Application/Lab (hour/week) | Local Credits | ECTS |
|---|---|---|---|---|---|
| IE 355 | Fall/Spring | 2 | 2 | 3 | 6 |
| Prerequisites |
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| Course Language | English | ||||||||
| Course Type | Elective | ||||||||
| Course Level | First Cycle | ||||||||
| Mode of Delivery | - | ||||||||
| Teaching Methods and Techniques of the Course | Problem SolvingLecture / Presentation | ||||||||
| Course Coordinator | |||||||||
| Course Lecturer(s) | |||||||||
| Assistant(s) | - | ||||||||
| Course Objectives | The aim of this course is to teach students building mathematical models and heuristic solution algorithms of real-life problems and to enable them solving the complex problems encountered in business. |
| Learning Outcomes | The students who succeeded in this course;
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| Course Description | Topics of this course include developing mathematical models and heuristic solution algorithms for essential Industrial Systems Engineering problems. During the course, IBM ILOG OPL Development Studio will be used to code and solve mathematical models and heuristic algorithms. |
| Related Sustainable Development Goals |   |
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| Core Courses | |
| Major Area Courses | X | |
| Supportive Courses | ||
| Media and Managment Skills Courses | ||
| Transferable Skill Courses |
| Week | Subjects | Required Materials |
| 1 | Introduction to Mathematical Modeling and OPL | IBM ILOG CPLEX OPTIMIZATION STUDIO (OPL) Documentation version 16, A Short Introduction to OPL |
| 2 | Building a Basic Mathematical Model: indexed decision variables, parameters and constraints | Ch 3, Winston, W. L., Operations Research: Applications and Algorithms, Duxbury Press |
| 3 | Building a Basic Mathematical Model: a production planning problem model | Ch 3, Winston, W. L., Operations Research: Applications and Algorithms, Duxbury Press |
| 4 | Building Linear Programming Models I : Workforce Planning model | Ch 3, Winston, W. L., Operations Research: Applications and Algorithms, Duxbury Press |
| 5 | Building Linear Programming Models II: CPM model | Ch 8, Winston, W. L., Operations Research: Applications and Algorithms, Duxbury Press |
| 6 | Building Linear Programming Models III: Supply planning model | Ch 9, Hillier, F. S., and Lieberman, G. J., Introduction to Operations Research, Tentth Edition, 2015 Mc Graw-Hill |
| 7 | Linearizing Logical Forms with Binary Variables | Ch 6, Sierksma, G. Linear and Integer Programming Theory and Practice, Marcel Dekker Inc. Second Edition |
| 8 | Building Integer Programming Models: Modeling integer programming models with conditional decisions | Ch 6-7, Sierksma, G. Linear and Integer Programming Theory and Practice, Marcel Dekker Inc. Second Edition |
| 9 | Building Integer Programming Models: set packing, covering and partitioning problems | Ch 6-7, Sierksma, G. Linear and Integer Programming Theory and Practice, Marcel Dekker Inc. Second Edition Ch 19, Winston, W. L., Operations Research: Applications and Algorithms, Duxbury Press |
| 10 | Algorithm development and programming with ILOG OPL | IBM ILOG CPLEX OPTIMIZATION STUDIO (OPL) Documentation version 16 |
| 11 | Quadratic Assignment Problem and Model Formulation | Ch 11, Rardin, R. L., Optimization in Operations Research, 1998, Prentice-Hall |
| 12 | Traveling Salesman Problem and Model Formulation, Heuristic Solution Algorithms | Ch 9, Winston, W. L., Operations Research: Applications and Algorithms, Duxbury Press |
| 13 | Cutting Stock Problem and Model Formulation | Ch 10, Winston, W. L., Operations Research: Applications and Algorithms, Duxbury Press |
| 14 | Project Presentations | |
| 15 | Review of the semester | |
| 16 | Final Exam |
| Course Notes/Textbooks | Operations Research: Applications and Algorithms, Wayne L. Winston, Duxbury Press, ISBN 0-534 20971-8., Introduction to Operations Research, Frederick S. Hillier, Gerald J. Lieberman, Tenth Edition, 2015 Mc Graw-Hill, ISBN: 978-007-126767-0 Linear and Integer Programming Theory and Practice, Gerard Sierksma, Marcel Dekker Inc., Second Edition, ISBN 978-0824706739 Optimization in Operations Research, Ronald L.Rardin, Prentice Hall, ISBN : 0-02-398415-5 |
| Suggested Readings/Materials | Logic and Integer Programming, H. Paul Williams, Springer, ISBN 978-0387922799 M. L. Pinedo, Scheduling: Theory, Algorithms, and Systems, 2005, Springer, ISBN 978-0387789347 |
| Semester Activities | Number | Weighting |
| Participation | 1 | 20 |
| Laboratory / Application | 1 | 20 |
| Field Work | ||
| Quizzes / Studio Critiques | 1 | 15 |
| Portfolio | ||
| Homework / Assignments | ||
| Presentation / Jury | 1 | 15 |
| Project | 1 | 30 |
| Seminar / Workshop | ||
| Oral Exam | ||
| Midterm | ||
| Final Exam | ||
| Total |
| Weighting of Semester Activities on the Final Grade | 4 | 70 |
| Weighting of End-of-Semester Activities on the Final Grade | 1 | 30 |
| Total |
| Semester Activities | Number | Duration (Hours) | Workload |
|---|---|---|---|
| Course Hours (Including exam week: 16 x total hours) | 16 | 2 | 32 |
| Laboratory / Application Hours (Including exam week: 16 x total hours) | 16 | 2 | |
| Study Hours Out of Class | 14 | 4 | 56 |
| Field Work | |||
| Quizzes / Studio Critiques | 1 | 20 | |
| Portfolio | |||
| Homework / Assignments | |||
| Presentation / Jury | 1 | 10 | |
| Project | 1 | 30 | |
| Seminar / Workshop | |||
| Oral Exam | |||
| Midterms | |||
| Final Exams | |||
| Total | 180 |
| # | Program Competencies/Outcomes | * Contribution Level | ||||
1 | 2 | 3 | 4 | 5 | ||
| 1 | To have adequate knowledge in Mathematics, Science and Industrial Engineering; to be able to use theoretical and applied information in these areas to model and solve Industrial Engineering problems. | X | ||||
| 2 | To be able to identify, formulate and solve complex Industrial Engineering problems by using state-of-the-art methods, techniques and equipment; to be able to select and apply proper analysis and modeling methods for this purpose. | X | ||||
| 3 | To be able to analyze a complex system, process, device or product, and to design with realistic limitations to meet the requirements using modern design techniques. | X | ||||
| 4 | To be able to choose and use the required modern techniques and tools for Industrial Engineering applications; to be able to use information technologies efficiently. | X | ||||
| 5 | To be able to design and do simulation and/or experiment, collect and analyze data and interpret the results for investigating Industrial Engineering problems and Industrial Engineering related research areas. | X | ||||
| 6 | To be able to work efficiently in Industrial Engineering disciplinary and multidisciplinary teams; to be able to work individually. | X | ||||
| 7 | To be able to communicate effectively in Turkish, both orally and in writing; to be able to author and comprehend written reports, to be able to prepare design and implementation reports, to present effectively; to be able to give and receive clear and comprehensible instructions | |||||
| 8 | To have knowledge about contemporary issues and the global and societal effects of Industrial Engineering practices on health, environment, and safety; to be aware of the legal consequences of Industrial Engineering solutions. | |||||
| 9 | To be aware of professional and ethical responsibility; to have knowledge of the standards used in Industrial Engineering practice. | |||||
| 10 | To have knowledge about business life practices such as project management, risk management, and change management; to be aware of entrepreneurship and innovation; to have knowledge about sustainable development. | |||||
| 11 | To be able to collect data in the area of Industrial Engineering; to be able to communicate with colleagues in a foreign language. | |||||
| 12 | To be able to speak a second foreign at a medium level of fluency efficiently. | |||||
| 13 | To recognize the need for lifelong learning; to be able to access information, to be able to stay current with developments in science and technology; to be able to relate the knowledge accumulated throughout the human history to Industrial Engineering. | X | ||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest
