Nevşehir Hacı Bektaş Veli University Course Catalogue
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| Year/Semester of Study | 1 / Fall Semester | ||||
| Level of Course | 3rd Cycle Degree Programme | ||||
| Type of Course | Optional | ||||
| Department | BUSINESS ADMINISTRATION - PRODUCTION MANAGEMENT AND MARKETING (DOCTORATE DEGREE) | ||||
| Pre-requisities and Co-requisites | None | ||||
| Mode of Delivery | Face to Face | ||||
| Teaching Period | 14 Weeks | ||||
| Name of Lecturer | NURİ ÖZGÜR DOĞAN (nodogan@nevsehir.edu.tr) | ||||
| Name of Lecturer(s) | NURİ ÖZGÜR DOĞAN, | ||||
| Language of Instruction | Turkish | ||||
| Work Placement(s) | None | ||||
| Objectives of the Course | |||||
| The objective of this course that can be seen as the advanced form of Operations Research; is to provide the students with the advanced mathematical models and optimization methods. | |||||
| Learning Outcomes | PO | MME | |
| The students who succeeded in this course: | |||
| LO-1 | Be able to assess different types of decision making methods more systematically. |
PO-10 Can understand new, different and/or complex issues, design an original studies and investigate them. |
Examination Term Paper |
| LO-2 | Be able to discuss the non-linear programming. |
PO-10 Can understand new, different and/or complex issues, design an original studies and investigate them. |
Examination Term Paper |
| LO-3 | Be able to recognize heuristic methods. |
PO-10 Can understand new, different and/or complex issues, design an original studies and investigate them. |
Examination Term Paper |
| LO-4 | Be able to define artificial intelligence algorithms. |
PO-10 Can understand new, different and/or complex issues, design an original studies and investigate them. |
Examination Term Paper |
| PO: Programme Outcomes MME:Method of measurement & Evaluation |
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| Course Contents | ||
| In the context of this course basics of non-linear programming, Kuhn-Tucker conditions, the geometric interpretation of Kuhn-Tucker conditions, Kuhn-Tucker conditions and improving directions, solution via Kuhn-Tucker conditions, improving searching method, separable programming, genetic algorithm and simulated annealing will be discussed. | ||
| Weekly Course Content | ||
| Week | Subject | Learning Activities and Teaching Methods |
| 1 | Introduction | Lecturing |
| 2 | Basics of Non-linear Programming | Lecturing |
| 3 | Non-linear Programming | Lecturing |
| 4 | Kuhn-Tucker Conditions | Lecturing |
| 5 | The geometric interpretation of Kuhn-Tucker conditions | Lecturing |
| 6 | Kuhn-Tucker conditions and improving directions | Lecturing |
| 7 | Solution via Kuhn-Tucker conditions | Lecturing and Problem solving method |
| 8 | mid-term exam | |
| 9 | Solution via Kuhn-Tucker conditions | Lecturing and Problem solving method |
| 10 | Improving searching method | Lecturing |
| 11 | Separable programming | Lecturing |
| 12 | Separable programming | Lecturing and Problem solving method |
| 13 | Genetic algorithm | Lecturing |
| 14 | Simulated annealing | Lecturing |
| 15 | Evaluating the projects | Project presentation |
| 16 | final exam | |
| Recommend Course Book / Supplementary Book/Reading | ||
| 1 | Bazaraa, Mokhtar S.; Nonlinear Programming: Theory and Algorithms, 1. Baskı, John Wiley and Sons, New York, 1979. | |
| 2 | Taha, Hamdy A.; Yoneylem Arastirmasi, (Ceviri: S.A. Baray; S. Esnaf), 6. Baski, Literatur Yayincilik, Istanbul, 2000. | |
| 3 | Hillier, F. S., G. J. Lieberman; Introduction to Mathematical Programming, McGraw Hill, New York, 1995. | |
| Required Course instruments and materials | ||
| 1. Taha, Hamdy A.; Yoneylem Arastirmasi, (Ceviri: S.A. Baray; S. Esnaf), 6. Baski, Literatur Yayincilik, Istanbul, 2000. 2. Bazaraa, Mokhtar S.; Nonlinear Programming: Theory and Algorithms, 1. Bask?, John Wiley and Sons, New York, 1979. 3. Hillier, F. S., G. J. Lieberman; Introduction to Mathematical Programming, McGraw Hill, New York, 1995. 4. Winston, W. L., Introduction to Mathematical Programming: Applications and Algorithms, 2nd Edition, ITP-Duxbury, 1997. | ||
| Assessment Methods | |||
| Type of Assessment | Week | Hours | Weight(%) |
| mid-term exam | 8 | 2 | 30 |
| Other assessment methods | |||
| 1.Oral Examination | |||
| 2.Quiz | |||
| 3.Laboratory exam | |||
| 4.Presentation | |||
| 5.Report | |||
| 6.Workshop | |||
| 7.Performance Project | |||
| 8.Term Paper | 15 | 1 | 10 |
| 9.Project | |||
| final exam | 16 | 2 | 60 |
| Student Work Load | |||
| Type of Work | Weekly Hours | Number of Weeks | Work Load |
| Weekly Course Hours (Theoretical+Practice) | 3 | 14 | 42 |
| Outside Class | |||
| a) Reading | 5 | 14 | 70 |
| b) Search in internet/Library | 2 | 14 | 28 |
| c) Performance Project | 0 | ||
| d) Prepare a workshop/Presentation/Report | 0 | ||
| e) Term paper/Project | 1 | 14 | 14 |
| Oral Examination | 0 | ||
| Quiz | 0 | ||
| Laboratory exam | 0 | ||
| Own study for mid-term exam | 2 | 7 | 14 |
| mid-term exam | 2 | 1 | 2 |
| Own study for final exam | 3 | 7 | 21 |
| final exam | 2 | 1 | 2 |
| 0 | |||
| 0 | |||
| Total work load; | 193 | ||