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| Course Description |
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| Course Name |
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Introduction To Modeling And Optimization |
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| Course Code |
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ENM212 |
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| Course Type |
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Compulsory |
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| Level of Course |
: |
First Cycle |
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| Year of Study |
: |
2 |
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| Course Semester |
: |
Spring (16 Weeks) |
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| ECTS |
: |
4 |
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| Name of Lecturer(s) |
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Assoc.Prof.Dr. ALİ KOKANGÜL
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| Learning Outcomes of the Course |
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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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| Mode of Delivery |
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Face-to-Face |
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| Prerequisites and Co-Prerequisites |
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None |
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| Recommended Optional Programme Components |
: |
None |
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| Aim(s) of Course |
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Gain the ability to verbal and mathematical description of real-life optimization problems, and drive solutions from the constructed mathematical models using computer software packages such as LINGO. |
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| Course Contents |
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Modeling: Physical models, mathematical models, and so on.; Mathematical modeling stages, the classification of mathematical programming methods, components of mathematical models: the decision variables, objective function and constraints; global and local solutions; verbal and mathematical expression of real-life optimization problems; graphical methods; linear programming: simplex algorithm; Big-M method, duality and sensitivity analysis; optimization computer package programs that are being used, the solution of mathematical models using LINGO package program. |
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| Language of Instruction |
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Turkish |
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| Work Place |
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Department Classroom |
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Course Outline /Schedule (Weekly) Planned Learning Activities |
| Week | Subject | Student's Preliminary Work | Learning Activities and Teaching Methods |
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1 |
Modeling: Physical models, mathematical models |
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2 |
Mathematical modeling stages |
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3 |
the classification of mathematical programming methods, |
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4 |
Components of mathematical models: the decision variables, objective function and constraints
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5 |
Global and local solutions
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6 |
Verbal and mathematical expression of real-life optimization problems
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7 |
Midterm axam |
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8 |
Graphical methods
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Grafical method |
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9 |
Linear programming
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|
10 |
Simplex Method |
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Simplex Method |
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11 |
Big-M method
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Büyük M metodu |
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12 |
Duality and
Sensitivity analysis
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Duality and
Sensitivity analysis |
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13 |
Applicatins to the real-life problems
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14 |
Optimization computer package programs that are being used,
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15 |
Drive solution of the mathematical models using LINGO package program. |
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16/17 |
Final 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
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 |
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Mid-term Exams (Written, Oral, etc.) |
1 |
50 |
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Homeworks/Projects/Others |
0 |
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
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100 |
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Rate of Final Assessments to Success
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60 |
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Total |
100 |
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| Contribution of the Course to Key Learning Outcomes |
| # | Key Learning Outcome | Contribution* |
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1 |
Can collect and analyze data required for industrial engineering problems ,develops and evaluates alternative solutions. |
4 |
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2 |
Has sufficient background on topics related to mathematics, physical sciences and industrial engineering. |
4 |
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3 |
Gains ability to use the acquired theoretical knowledge on basic sciences and industrial engineering for describing, formulating and solving an industrial engineering problem, and to choose appropriate analytical and modeling methods. |
5 |
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4 |
Gains ability to analyze a service and/or manufacturing system or a process and describes, formulates and solves its problems . |
3 |
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5 |
Gains ability to choose and apply methods and tools for industrial engineering applications. |
4 |
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6 |
Can access information and to search/use databases and other sources for information gathering. |
4 |
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7 |
Works efficiently and takes responsibility both individually and as a member of a multi-disciplinary team. |
3 |
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8 |
Appreciates life time learning; follows scientific and technological developments and renews himself/herself continuously. |
3 |
|
9 |
Can use computer software in industrial engineering along with information and communication technologies. |
5 |
|
10 |
Can use oral and written communication efficiently. |
2 |
|
11 |
Has a conscious understanding of professional and ethical responsibilities. |
2 |
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12 |
Uses English skills to follow developments in industrial engineering and to communicate with people in his/her profession. |
3 |
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13 |
Has a necessary consciousness on issues related to job safety and health, legal aspects of environment and engineering practice. |
3 |
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14 |
Becomes competent on matters related to project management, entrepreneurship, innovation and has knowledge about current matters in industrial engineering. |
3 |
| * 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 |
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Class Time (Exam weeks are excluded) |
14 |
3 |
42 |
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Out of Class Study (Preliminary Work, Practice) |
14 |
3 |
42 |
| Assesment Related Works |
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Homeworks, Projects, Others |
0 |
0 |
0 |
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Mid-term Exams (Written, Oral, etc.) |
1 |
10 |
10 |
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Final Exam |
1 |
10 |
10 |
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Total Workload: | 104 |
| Total Workload / 25 (h): | 4.16 |
| ECTS Credit: | 4 |
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