| Course Description |
|
| Course Name |
: |
Operational Research II |
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| Course Code |
: |
IEM 712 |
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| Course Type |
: |
Optional |
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| Level of Course |
: |
Second Cycle |
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| Year of Study |
: |
1 |
|
| Course Semester |
: |
Spring (16 Weeks) |
|
| ECTS |
: |
6 |
|
| Name of Lecturer(s) |
: |
Asst.Prof.Dr. ERSİN KIRAL
|
|
| Learning Outcomes of the Course |
: |
Gains ability to use quantitative techniques in decision-making process; problem definition, model building and solving
Gains ability of analytical thinking
Gains the ability of scientific decision-making
|
|
| 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 |
: |
The aim of the course is to provide the students with the capability of using quantitative techniques for decision making; problem identification, model building and solving |
|
| Course Contents |
: |
Deterministic inventory models, stochastic inventory models, basic probability distributions (normal, binomial and Poisson distributions), single-channel and multi-channel queuing systems, deterministic dynamic programming, Markov chains, decision analysis and game theory, model building with simulation, classical optimization problems, uconstrained problems, constrained problems, non-linear programming algorithms, quadratic programming |
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| Language of Instruction |
: |
Turkish |
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| Work Place |
: |
Theoretical courses will be taken in classrooms; computer labs will be used for application |
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Course Outline /Schedule (Weekly) Planned Learning Activities |
| Week | Subject | Student's Preliminary Work | Learning Activities and Teaching Methods |
|
1 |
Deterministic inventory models |
Reading relevant parts in the source books according to the weekly program |
Discussion topics in classroom, computer application in lab. |
|
2 |
Stochastic inventory models |
Reading relevant parts in the source books according to the weekly program |
Discussion topics in classroom, computer application in lab. |
|
3 |
Basic probability distributions (normal, binomial and Poisson distributions) |
Reading relevant parts in the source books according to the weekly program |
Discussion topics in classroom, computer application in lab. |
|
4 |
Single-channel and multi-channel queuing systems |
Reading relevant parts in the source books according to the weekly program |
Discussion topics in classroom, computer application in lab. |
|
5 |
Deterministic dynamic programming |
Reading relevant parts in the source books according to the weekly program |
Discussion topics in classroom, computer application in lab. |
|
6 |
Modelin with Markov chains |
Reading relevant parts in the source books according to the weekly program |
Discussion topics in classroom, computer application in lab. |
|
7 |
Decision analysis and game theory |
Reading relevant parts in the source books according to the weekly program |
Discussion topics in classroom, computer application in lab. |
|
8 |
Midterm Exam |
|
|
|
9 |
Model building with simulation |
Reading relevant parts in the source books according to the weekly program |
Discussion topics in classroom, computer application in lab. |
|
10 |
Classical optimization problems |
Reading relevant parts in the source books according to the weekly program |
Discussion topics in classroom, computer application in lab. |
|
11 |
Unconstrained problems |
Reading relevant parts in the source books according to the weekly program |
Discussion topics in classroom, computer application in lab. |
|
12 |
Constrained problems |
Reading relevant parts in the source books according to the weekly program |
Discussion topics in classroom, computer application in lab. |
|
13 |
Non-linear programming algorithms |
Reading relevant parts in the source books according to the weekly program |
Discussion topics in classroom, computer application in lab. |
|
14 |
Gradient descent algorithm |
Reading relevant parts in the source books according to the weekly program |
Discussion topics in classroom, computer application in lab. |
|
15 |
Quadratic programming |
Reading relevant parts in the source books according to the weekly program |
Discussion topics in classroom, computer application in lab. |
|
16/17 |
Final Exam |
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| Contribution of the Course to Key Learning Outcomes |
| # | Key Learning Outcome | Contribution* |
|
1 |
Explains Econometric concepts |
4 |
|
2 |
Equipped with the foundations of Economics, develops Economic models |
5 |
|
3 |
Models problems using the knowledge of Mathematics, Statistics, and Econometrics |
4 |
|
4 |
Acquires the ability to analyze, benchmark, evaluate and interpret at conceptual levels to develop solutions to problems |
3 |
|
5 |
Collects, edits, and analyzes data |
5 |
|
6 |
Uses advanced software packages concerning Econometrics, Statistics, and Operation Research |
5 |
|
7 |
Develops the ability to use different resources in an area which has not been studied in the scope of academic rules, synthesizes the information gathered, and gives effective presentations |
5 |
|
8 |
Speaks Turkish and at least one other foreign language in accordance with the requirements of academic and business life. |
4 |
|
9 |
Questions traditional approaches and their implementation and develops alternative study programs when required |
5 |
|
10 |
Recognizes and implements social, scientific, and professional ethic values |
2 |
|
11 |
Gives a consistent estimate for the model and analyzes and interprets its results |
3 |
|
12 |
Takes responsibility individually and/or as a member of a team; leads a team and works effectively |
3 |
|
13 |
Defines the concepts of statistics, operations research and mathematics. |
4 |
|
14 |
Knowing the necessity of life-long learning, follows the latest developments in the field of study and improves himself continiously |
2 |
|
15 |
Follows the current issues, and interprets the data about economic and social events. |
3 |
|
16 |
Understands and interprets the feelings, thoughts and behaviours of people and expresses himself/herself orally and in written form efficiently |
4 |
| * Contribution levels are between 0 (not) and 5 (maximum). |
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