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Description of Individual Course Units| Course Unit Code | Course Unit Title | Type of Course Unit | Year of Study | Semester | Number of ECTS Credits | | UT309B.8 | operations research | Elective | 3 | 5 | 4 |
| | Level of Course Unit | | First Cycle | | Objectives of the Course | | The course aims to give to students to ability of developing operations research models and applying these models to the main areas of business such as production, marketing and finance. | | Name of Lecturer(s) | | | Learning Outcomes | | 1 | Describe various terms about building model in Linear Programming (LP). | | 2 | Distinguish various LP techniques. | | 3 | Prepare the most appropriate LP model for determined sector (problem). | | 4 | Examine the appropriateness of used model to real situation. | | 5 | Build a new model removing deficiencies of used LP model. |
| | Mode of Delivery | | Normal Education | | Prerequisites and co-requisities | | None | | Recommended Optional Programme Components | | None | | Course Contents | | Modeling linear programming problems, graphical solution, simplex method, LP applications, duality, primal - dual relationships, economic interpretation of duality, sensitivity analysis, integer programming, goal programming, applications with Lindo packages. | | Weekly Detailed Course Contents | |
| 1 | Course objectives and outlines, The methodology of operations research, The fundamental concepts of linear programming | | | | 2 | Definitions, assumptions and general model structure of LP, Formulating the Linear Programming problems | | | | 3 | Model building, Characteristics of the LP models, Models of cost, revenue and profit | | | | 4 | Solution Methodologies of LP Problems, Graphical solution, Graphical solution procedure for maximization and minimization problems | | | | 5 | Special cases of graphical solution, Infeasibility, Unboundedness, Alternative optimal solutions | | | | 6 | The simplex algorithm, Setting up the initial simplex tableau, Arranging the optimal solution | | | | 7 | Summary of the simplex method, Interpreting the obtained results from the optimal simplex tableau | | | | 8 | Mid-term exam | | | | 9 | Special cases of simplex solution method, Degeneracy of the simplex algorithm, Infeasibility Unboundedness, Alternative optimal solutions | | | | 10 | Duality, Economic Interpretation of the Dual Variables | | | | 11 | Sensitivity analysis, Changes in objective functions' coefficients, Changes in right-hand-side values | | | | 12 | Introducing softwares for solving LP models (Lindo, Lingo, WinQSB, QM, and Tora) | | | | 13 | Integer programming models, Formulating integer programming problems, Solution of the integer programming problems with gomory column generation procedure | | | | 14 | Goal programming, Model formulation, Graphical interpretation of goal programming | | | | 15 | Review | | | | 16 | End-of-term exam | | |
| | Recommended or Required Reading | | OPERATIONS RESEARCH: APPLICATIONS AND ALGORITHMS,
W. L. WINSTON, THOMPSON – BROOKS/COLE, 2004 | | Planned Learning Activities and Teaching Methods | | | Assessment Methods and Criteria | |
| Midterm Examination | 1 | 100 | | SUM | 100 | |
| Final Examination | 1 | 100 | | SUM | 100 | | Term (or Year) Learning Activities | 40 | | End Of Term (or Year) Learning Activities | 60 | | SUM | 100 |
| | Language of Instruction | | Turkish | | Work Placement(s) | | None |
| | Workload Calculation | |
| Midterm Examination | 1 | 1 | 1 | | Final Examination | 1 | 2 | 2 | | Attending Lectures | 14 | 2 | 28 | | Individual Study for Mid term Examination | 1 | 24 | 24 | | Individual Study for Final Examination | 1 | 25 | 25 | | Homework | 14 | 2 | 28 | |
| Contribution of Learning Outcomes to Programme Outcomes | | LO1 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 3 | 5 | | LO2 | 4 | 3 | 3 | 4 | 3 | 4 | 3 | 4 | 3 | 3 | | LO3 | 4 | 3 | 3 | 3 | 3 | 5 | 4 | 3 | 4 | 4 | | LO4 | 3 | 3 | 3 | 3 | 5 | 3 | 3 | 4 | 4 | 3 | | LO5 | 3 | 3 | 3 | 3 | 4 | 4 | 3 | 3 | 3 | 4 |
| | * Contribution Level : 1 Very low 2 Low 3 Medium 4 High 5 Very High |
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