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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 | | İŞL302B2 | Operations Research II | Compulsory | 3 | 6 | 5 |
| | Level of Course Unit | | First Cycle | | Objectives of the Course | | The aim of Operations Research 1 course is to teach the use of mathematical and analytical methods in decision making processes. This course is often referred to as operational research, decision analysis or operations research. | | Name of Lecturer(s) | | Doç. Dr. hakan PABUÇCU | | Learning Outcomes | | 1 | Analysing Decision Making Processes:
Students develop the ability to identify and analyse real-world problems.
By understanding data collection processes, they try to comprehend all aspects of the problem. | | 2 | Mathematical Modelling Skills:
Students gain the ability to mathematically model the problems they encounter.
They use appropriate mathematical expressions to define the problem and identify constraints. | | 3 | Application of Optimisation Techniques:
Students learn optimisation techniques such as linear programming, integer programming and dynamic programming.
Using these techniques, they develop their ability to optimise decision-making problems and find the best solution. | | 4 | Using Decision Support Systems:
Students learn to solve problems using decision support systems and software.
By using these systems effectively, they can provide knowledge-based support in decision-making processes. | | 5 | Decision Analysis Applications:
Students learn applications of decision analysis in various fields such as industrial, business, economics and engineering.
They apply the techniques learnt in practice by working on applied projects for real-world problems. |
| | Mode of Delivery | | Normal Education | | Prerequisites and co-requisities | | None | | Recommended Optional Programme Components | | None | | Course Contents | | Understanding Decision Making Processes
Developing Mathematical Modelling Skills
Application of Optimisation Techniques
Using Decision Support Systems
Decision Analysis Applications
**Modelling of linear programming problems, graphical solution, simplex method, DP applications, duality, primal-dual relations, economic interpretation of duality, sensitivity analysis, integer programming, goal programming, applications with Lindo package. | | Weekly Detailed Course Contents | |
| 1 | Definition and importance of transportation models
Classification of transportation models | | | | 2 | Examples and application areas of transportation problems
Transportation modeling process | | | | 3 | Basic concepts of transportation problems
Mathematical formulation of transportation problems
Linear programming basics
Solution of transportation problems with linear programming | | | | 4 | Classification of transportation model solution methods
Simple and advanced solution methods: North-West Corner Rule, Lowest Order Cell Method, MODI, Vogel
Comparison and advantages of solution methods | | | | 5 | Classification of transportation model solution methods
Simple and advanced solution methods: North-West Corner Rule, Lowest Order Cell Method, MODI, Vogel
Comparison and advantages of solution methods | | | | 6 | Classification of transportation model solution methods
Simple and advanced solution methods: North-West Corner Rule, Lowest Order Cell Method, MODI, Vogel
Comparison and advantages of solution methods | | | | 7 | Mid term exam | | | | 8 | Basic concepts of queuing theory Queuing system models and components | | | | 9 | Application areas of queuing theory Examples of queuing theory problems | | | | 10 | Classification of queuing models
Queue performance measures: Waiting time, queue length, service time | | | | 11 | Mathematical formulation of queuing models
Queueing model solution methods: Analytical and simulation | | | | 12 | Basic principles of queue simulation
Creating the simulation model: Inputs, outputs, processes | | | | 13 | Use of simulation software
Queueing simulation applications and analysis | | | | 14 | Customized applications for special transportation models or queueing theory problems chosen by the students
Group work or project presentations | | |
| | Recommended or Required Reading | | Yöneylem Araştırması Ahmet Öztürk Ekin Yayınevi
Operations Research: An Introduction, Hamdy Taha, Ninth Ed., Pearson, 2011. Introduction to Operations Research, Frederich S. Hillier, Gerald J. Lieberman, Ninth Ed. McGraw-Hill, 2010 Operations Research, T. L. Winston, PWS Publishing Company, 1997. | | 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 | 4 | 56 | | Individual Study for Mid term Examination | 1 | 24 | 24 | | Individual Study for Final Examination | 1 | 25 | 25 | | Homework | 14 | 3 | 42 | |
| Contribution of Learning Outcomes to Programme Outcomes | | LO1 | 4 | 4 | 5 | 5 | 3 | 2 | 3 | 2 | 5 | 4 | | LO2 | 4 | 1 | 5 | 1 | 4 | 3 | 2 | 3 | 2 | 4 | | LO3 | 1 | 1 | 1 | 2 | 2 | 1 | 2 | 3 | 1 | 2 | | LO4 | 3 | 3 | 2 | 1 | 3 | 2 | 3 | 2 | 3 | 3 | | LO5 | 1 | 2 | 1 | 2 | 2 | 3 | 2 | 2 | 3 | 3 |
| | * Contribution Level : 1 Very low 2 Low 3 Medium 4 High 5 Very High |
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