School of Engineering \ Industrial Engineering
Course Credit
ECTS Credit
Course Type
Instructional Language
Programs that can take the course
Industrial Engineering, Artificial Intelligence Engineering
Computer Engineering (as department elective)
Operations Research and its history, linear programming, linear programming models, optimization with linear programming, simplex algorithm, computer based linear programming, duality and sensitivity analysis.
Textbook and / or References
1.Wayne L. Winston, “Operations Research, Applications and Algorithms”, 4th edition, Cengage Learning, ISBN 0357907817, 9780357907818.
1. Wayne L. Winston, “Operations Research, Applications and Algorithms”, 4th edition, Cengage Learning, ISBN 0357907817, 9780357907818.
2. D. Bertsimas and J. N. Tsitsiklis, “Introduction to Linear Optimization”, Athena Scientific, 1997.
3. M. Ç. Pınar. “Doğrusal Optimizasyondan Çıkış Ders Notları”, Seçkin Yayıncılık, 2019.
4. H. Taha, “Introduction to Operations Research”, 7th edition, Prentice Hall, 2003.
5. F.S. Hillier and G.J. Lieberman, “Introduction to Operations Research”, Holden-Day, 1967.
This is an introductory course at undergraduate level to mathematical modelling and optimization. The primary objective of the course is to help students have a general perspective about operations research and its methods. The main focus will be on constructing the mathematical models of real life problems and solving them using linear programming techniques. At the end of the course, students are expected to have the ability to exploit mathematical modelling techniques to solve decision making problems they face in their career as industrial or artificial intelligence engineers.
1. gain the ability to model any real-life problem as a linear programming formulation, or a relaxed version of it with some assumptions;
2. gain the ability to solve their formulations using a commercial software;
3. gain the ability to interpret the effects of changes in problem parameters on the optimal solution;
4. gain the ability to create dual models of their formulations;
5. gain knowledge about the relationship between the dual problem and the original problem.
Week 1: Introduction to Linear Prog. and Modeling
Week 2: Linear Prog. and Modeling Examples
Week 3: Geometry of Linear Prog.
Week 4: Linear Prog. Examples and Computer Based Solution Methods
Week 5: Linear Prog. Examples and Computer Based Solution Methods
Week 6: Simplex Algorithm
Week 7: Simplex Algorithm
Week 8: Sensitivity Analysis for Linear Prog.
Week 9: Sensitivity Analysis for Linear Prog.
Week 10: Duality
Week 11: Duality
Week 12: Sensitivity Analysis and Duality for Linear Prog.
Tentative Assesment Methods
• Midterm 30 %
• Final 40 %
• Attendance 5 %
• Quizzes 25 %
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Course Outcome
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C
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B
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A, B, C
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D
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C
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A, C
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A, B, C
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D
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C
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A, C
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A
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C
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A, C
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A
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C
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