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| Course Description |
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| Course Name |
: |
Operations Research Modeling and Applications |
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| Course Code |
: |
EM-537 |
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| Course Type |
: |
Optional |
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| Level of Course |
: |
Second Cycle |
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| Year of Study |
: |
1 |
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| Course Semester |
: |
Fall (16 Weeks) |
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| ECTS |
: |
6 |
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| Name of Lecturer(s) |
: |
Assoc.Prof.Dr. ALİ KOKANGÜL
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| Learning Outcomes of the Course |
: |
Learns to construct mathematical models of real-life problems, select the most appropriate optimization method to the constructed mathematical model and drive the solutions from the model using computer optimization programs such as LINGO.
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| Mode of Delivery |
: |
Face-to-Face |
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| Prerequisites and Co-Prerequisites |
: |
None |
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| Recommended Optional Programme Components |
: |
None |
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| Aim(s) of Course |
: |
It is aimed to provide detailed information on operation research techniques and ability to apply these techniques using LINDO package program. |
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| Course Contents |
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Modeling: Physical models, mathematical models, etc.; the classification of operations research techniques, components of mathematical models: the decision variables, objective function and constraints; global and local solutions; optimization steps; verbal and mathematical expression of real-life optimization problems; graphical methods; linear programming, nonlinear programming, duality and sensitivity analysis; optimization computer package programs that are being used, the solution of mathematical models using LINGO package program, applications of linear and nonlinear programming, Project presentations.
Construction mathematical models of real-life problems, selecting the most appropriate optimization method to the constructed mathematical model and driving the solutions from the model using computer optimization programs such as LINGO. |
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| Language of Instruction |
: |
Turkish |
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| Work Place |
: |
IE Classroom |
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Course Outline /Schedule (Weekly) Planned Learning Activities |
| Week | Subject | Student's Preliminary Work | Learning Activities and Teaching Methods |
|
1 |
Modeling: Physical models, mathematical models, and so on.; |
reading the related textbook chapter |
lecture, discussion |
|
2 |
the classification of operations research techniques |
reading the related textbook chapter |
lecture, discussion |
|
3 |
components of mathematical models: the decision variables, objective function and constraints |
reading the related textbook chapter |
lecture, discussion |
|
4 |
optimizasyonda global ve lokal çözümler |
reading the related textbook chapter |
lecture, discussion |
|
5 |
optimization steps |
reading the related textbook chapter |
lecture, discussion |
|
6 |
verbal and mathematical expression of real-life optimization problems |
reading the related textbook chapter |
lecture, discussion |
|
7 |
graphical methods |
reading the related textbook chapter |
lecture, discussion |
|
8 |
Midterm exam |
prepare for the exam |
written exam |
|
9 |
linear programming and its applications |
reading the related textbook chapter |
lecture, discussion |
|
10 |
nonlinear programming and its applications |
reading the related textbook chapter |
lecture, discussion |
|
11 |
duality and sensitivity analysis |
reading the related textbook chapter |
lecture, discussion |
|
12 |
computer package programs that are being used |
reading the related textbook chapter |
lecture, discussion |
|
13 |
the solution of mathematical models using LINGO package program |
reading the related textbook chapter |
lecture, discussion |
|
14 |
project presentation |
prepare a presentation |
presentation |
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15 |
project presentation |
prepare a presentation |
presentation |
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16/17 |
Final exam |
prepare for the exam |
written exam |
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Required Course Resources |
| Resource Type | Resource Name |
| Recommended Course Material(s) |
 Winston, W.L.,2004, Operations Research Applications and Algorithms, Fourth Edition
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| |
| Required Course Material(s) | |
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Assessment Methods and Assessment Criteria |
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Semester/Year Assessments |
Number |
Contribution Percentage |
|
Mid-term Exams (Written, Oral, etc.) |
1 |
50 |
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Homeworks/Projects/Others |
1 |
50 |
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Total |
100 |
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Rate of Semester/Year Assessments to Success |
40 |
| |
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Final Assessments
|
100 |
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Rate of Final Assessments to Success
|
60 |
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Total |
100 |
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| Contribution of the Course to Key Learning Outcomes |
| # | Key Learning Outcome | Contribution* |
|
1 |
Understand, interpret and apply knowledge in his/her field domain both in-depth and in-breadth by doing scientific research in industrial engineering. |
5 |
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2 |
Acquire comprehensive knowledge about methods and tools of industrial engineering and their limitations. |
4 |
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3 |
Work in multi-disciplinary teams and take a leading role and responsibility. |
4 |
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4 |
Identify, gather and use necessary information and data. |
5 |
|
5 |
Complete and apply the knowledge by using scarce and limited resources in a scientific way and integrate the knowledge into various disciplines. |
5 |
|
6 |
Keep up with the recent changes and applications in the field of Industrial Engineering and analyze these innovations when necessary. |
4 |
|
7 |
Work in multi-disciplinary teams, take a leading role and responsibility and develop solutions for complex problems. |
4 |
|
8 |
Analyze Industrial Engineering problems, develop innovative methods to solve the problems. |
5 |
|
9 |
Have the ability to propose new and/or original ideas and methods in developing innovative solutions for designing systems, components or processes. |
3 |
|
10 |
Design and perform analytical modeling and experimental research and analyze/solve complex matters emerged in this process. |
5 |
|
11 |
Follow, study and learn new and developing applications of industrial engineering. |
3 |
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12 |
Use a foreign language in verbal and written communication at least B2 level of European Language Portfolio. |
2 |
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13 |
Present his/her research findings systematically and clearly in oral and written forms in national and international platforms. |
4 |
|
14 |
Understand social and environmental implications of engineering practice. |
4 |
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15 |
Consider social, scientific and ethical values in the process of data collection, interpretation and announcement of the findings. |
5 |
| * Contribution levels are between 0 (not) and 5 (maximum). |
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| Student Workload - ECTS |
| Works | Number | Time (Hour) | Total Workload (Hour) |
| Course Related Works |
|
Class Time (Exam weeks are excluded) |
14 |
3 |
42 |
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Out of Class Study (Preliminary Work, Practice) |
14 |
6 |
84 |
| Assesment Related Works |
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Homeworks, Projects, Others |
1 |
12 |
12 |
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Mid-term Exams (Written, Oral, etc.) |
1 |
10 |
10 |
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Final Exam |
1 |
12 |
12 |
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Total Workload: | 160 |
| Total Workload / 25 (h): | 6.4 |
| ECTS Credit: | 6 |
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