| Course Name | Decision Theory |
| Code | Semester | Theory (hour/week) | Application/Lab (hour/week) | Local Credits | ECTS |
|---|---|---|---|---|---|
| IE 342 | Fall/Spring | 3 | 0 | 3 | 5 |
| Prerequisites |
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| Course Language | English | ||||||||
| Course Type | Elective | ||||||||
| Course Level | First Cycle | ||||||||
| Mode of Delivery | - | ||||||||
| Teaching Methods and Techniques of the Course | Lecture / Presentation | ||||||||
| Course Coordinator | |||||||||
| Course Lecturer(s) | |||||||||
| Assistant(s) | - | ||||||||
| Course Objectives | The objectives of this course are to familiarize students with the introductory knowledge on modelling, analysis and solution approaches for decision making situations under uncertainty, under risk, under certainty and in situations with multiple criteria. |
| Learning Outcomes | The students who succeeded in this course;
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| Course Description | This course is one of the basic sections of Operations Research, which studies a rational process for selecting the best of several alternatives. The “goodness” of a selected alternative depends on the quality of the data used in describing the decision situation. From this standpoint, a decisionmaking process can fall into one of three categories. 1. Decisionmaking under uncertainty in which the data cannot be assigned relative weights that represent their degree of relevance in the decision process. 2. Decisionmaking under risk in which the data can be described by probability distributions. 3. Decisionmaking under certainty in which the data are known deterministically. 4. Decision making in multicriteria environment. The main subjects of the course are the decision situation, decision rule, decision trees, information and the cost of additional information, utility theory, multiobjective problems, solution notions for such problems and methods for calculations efficient solutions for multiobjective problems, goal programming and the methods of analyzing solutions for goal programming problems. |
| Related Sustainable Development Goals | |
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| Core Courses | |
| Major Area Courses | X | |
| Supportive Courses | ||
| Media and Managment Skills Courses | ||
| Transferable Skill Courses |
| Week | Subjects | Required Materials |
| 1 | Introduction to the Course. Introduction to Decision Theory. Behavioral decision analysis. | |
| 2 | Decision making under certainty. Decision making under uncertainty. Decision making under risk | |
| 3 | Utility Theory. Single attribute utility. Probability-equivalence approach. | |
| 4 | Interpreting utility functions. Utility functions for nonmonetary attributes. | |
| 5 | The axioms of utility. Certainty equivalence approach. | |
| 6 | Attitudes towards risk. Risk premium. Decreasing and constant risk aversion. | |
| 7 | Midterm | |
| 8 | Value of information. | |
| 9 | Expected value of perfect information. | |
| 10 | Expected value of sample information. | |
| 11 | Multicriteria Decision Making. Goal Programming. | |
| 12 | Analytic Hierarchy Process. | |
| 13 | Multiattribute Utility Theory | |
| 14 | Outranking relations. | |
| 15 | Review | |
| 16 | Review |
| Course Notes/Textbooks | Lecture Notes |
| Suggested Readings/Materials | 1. Robert T. Clemen, Terence Reilly, Making Hard Decisions With Decision Tools, Duxbury Thomson Learning, 2001; ISBN13: 9780495015086; ISBN10: 0495015083. 2. Wayne L. Winston, Operations Research. Applications and Algorithms, Duxbury Press, Belmont, California, 1994. |
| Semester Activities | Number | Weighting |
| Participation | 1 | |
| Laboratory / Application | ||
| Field Work | ||
| Quizzes / Studio Critiques | 1 | 30 |
| Portfolio | ||
| Homework / Assignments | ||
| Presentation / Jury | ||
| Project | ||
| Seminar / Workshop | ||
| Oral Exam | ||
| Midterm | 1 | 30 |
| Final Exam | 1 | 40 |
| Total |
| Weighting of Semester Activities on the Final Grade | 3 | 65 |
| Weighting of End-of-Semester Activities on the Final Grade | 1 | 35 |
| Total |
| Semester Activities | Number | Duration (Hours) | Workload |
|---|---|---|---|
| Course Hours (Including exam week: 16 x total hours) | 16 | 3 | 48 |
| Laboratory / Application Hours (Including exam week: 16 x total hours) | 16 | ||
| Study Hours Out of Class | 14 | 3 | 42 |
| Field Work | |||
| Quizzes / Studio Critiques | 1 | 20 | |
| Portfolio | |||
| Homework / Assignments | |||
| Presentation / Jury | |||
| Project | |||
| Seminar / Workshop | |||
| Oral Exam | |||
| Midterms | 1 | 20 | |
| Final Exams | 1 | 20 | |
| Total | 150 |
| # | Program Competencies/Outcomes | * Contribution Level | ||||
1 | 2 | 3 | 4 | 5 | ||
| 1 | To have adequate knowledge in Mathematics, Science and Industrial Engineering; to be able to use theoretical and applied information in these areas to model and solve Industrial Engineering problems. | X | ||||
| 2 | To be able to identify, formulate and solve complex Industrial Engineering problems by using state-of-the-art methods, techniques and equipment; to be able to select and apply proper analysis and modeling methods for this purpose. | X | ||||
| 3 | To be able to analyze a complex system, process, device or product, and to design with realistic limitations to meet the requirements using modern design techniques. | |||||
| 4 | To be able to choose and use the required modern techniques and tools for Industrial Engineering applications; to be able to use information technologies efficiently. | X | ||||
| 5 | To be able to design and do simulation and/or experiment, collect and analyze data and interpret the results for investigating Industrial Engineering problems and Industrial Engineering related research areas. | |||||
| 6 | To be able to work efficiently in Industrial Engineering disciplinary and multidisciplinary teams; to be able to work individually. | |||||
| 7 | To be able to communicate effectively in Turkish, both orally and in writing; to be able to author and comprehend written reports, to be able to prepare design and implementation reports, to present effectively; to be able to give and receive clear and comprehensible instructions | |||||
| 8 | To have knowledge about contemporary issues and the global and societal effects of Industrial Engineering practices on health, environment, and safety; to be aware of the legal consequences of Industrial Engineering solutions. | |||||
| 9 | To be aware of professional and ethical responsibility; to have knowledge of the standards used in Industrial Engineering practice. | |||||
| 10 | To have knowledge about business life practices such as project management, risk management, and change management; to be aware of entrepreneurship and innovation; to have knowledge about sustainable development. | |||||
| 11 | To be able to collect data in the area of Industrial Engineering; to be able to communicate with colleagues in a foreign language. | |||||
| 12 | To be able to speak a second foreign at a medium level of fluency efficiently. | |||||
| 13 | To recognize the need for lifelong learning; to be able to access information, to be able to stay current with developments in science and technology; to be able to relate the knowledge accumulated throughout the human history to Industrial Engineering. | |||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest
