EMÜ659 - DYNAMIC DECISION MODELS
| Course Name | Code | Semester | Theory (hours/week) |
Application (hours/week) |
Credit | ECTS |
|---|---|---|---|---|---|---|
| DYNAMIC DECISION MODELS | EMÜ659 | Any Semester/Year | 3 | 0 | 3 | 10 |
| Prequisites | ||||||
| Course language | Turkish | |||||
| Course type | Elective | |||||
| Mode of Delivery | Face-to-Face | |||||
| Learning and teaching strategies | Lecture Question and Answer Problem Solving Project Design/Management Other: Lecture, question and answer, problem solving, project design/management, individual study. | |||||
| Instructor (s) | To be determined by the department | |||||
| Course objective | The objective of this course is to develop students? skills to build dynamic models for sequential decision making problems and find solutions for the optimality equations in order to characterize the optimal policies. | |||||
| Learning outcomes |
| |||||
| Course Content | Sequential/dynamic decision making problems Basic concepts and backward and forward model formulations for sequential decision making problems Optimality equations Finite horizon Markov decision processes Infinite horizon models Discounted Markov decision models Markov decision process with expected total reward criterion Solution methods and algorithms for sequential decision making problems Applications for sequential decision making models | |||||
| References | Puterman, M.L. (1994) Markov Decision Processes: Discrete Stochastic Dynamic Programming, 4th ed. John Wiley & Sons. Bertsekas, D.P. (1995) Dynamic Programming and Optimal Control, Athena Scientific. Tijms, H. (1994) Stochastic Models: An Algorithmic Approach, John Wiley & Sons. Ross, S.M. (1983) Introduction to Stochastic Dynamic Programming, Academic Press Up-to-date research articles about dynamic decision models and applications | |||||
Course outline weekly
| Weeks | Topics |
|---|---|
| Week 1 | Sequential decision models |
| Week 2 | Model formulations |
| Week 3 | Applications of sequential decision making models |
| Week 4 | Applications of sequential decision making models |
| Week 5 | Finite horizon Markov decision processes |
| Week 6 | Finite horizon Markov decision processes |
| Week 7 | Finite horizon Markov decision processes |
| Week 8 | Midterm exam |
| Week 9 | Infinite horizon models |
| Week 10 | Infinite horizon models |
| Week 11 | Discounted Markov decision models |
| Week 12 | Discounted Markov decision models |
| Week 13 | Expected Total Reward Criterion |
| Week 14 | Expected Total Reward Criterion/Project Presentations |
| Week 15 | Study for the Final Exam |
| Week 16 | Final exam |
Assesment methods
| Course activities | Number | Percentage |
|---|---|---|
| Attendance | 0 | 0 |
| Laboratory | 0 | 0 |
| Application | 0 | 0 |
| Field activities | 0 | 0 |
| Specific practical training | 0 | 0 |
| Assignments | 5 | 15 |
| Presentation | 1 | 3 |
| Project | 1 | 12 |
| Seminar | 0 | 0 |
| Midterms | 1 | 20 |
| Final exam | 1 | 50 |
| Total | 100 | |
| Percentage of semester activities contributing grade succes | 8 | 50 |
| Percentage of final exam contributing grade succes | 1 | 50 |
| Total | 100 | |
WORKLOAD AND ECTS CALCULATION
| Activities | Number | Duration (hour) | Total Work Load |
|---|---|---|---|
| Course Duration (x14) | 14 | 3 | 42 |
| Laboratory | 0 | 0 | 0 |
| Application | 0 | 0 | 0 |
| Specific practical training | 0 | 0 | 0 |
| Field activities | 0 | 0 | 0 |
| Study Hours Out of Class (Preliminary work, reinforcement, ect) | 13 | 7 | 91 |
| Presentation / Seminar Preparation | 1 | 12 | 12 |
| Project | 1 | 40 | 40 |
| Homework assignment | 5 | 12 | 60 |
| Midterms (Study duration) | 1 | 20 | 20 |
| Final Exam (Study duration) | 1 | 35 | 35 |
| Total Workload | 36 | 129 | 300 |
Matrix Of The Course Learning Outcomes Versus Program Outcomes
| D.9. Key Learning Outcomes | Contrubition level* | ||||
|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |
| 1. Reach the necessary knowledge and methods in engineering within the scope of advanced industrial engineering studies through scientific research and evaluate knowledge and methods and implement them. | X | ||||
| 2. Implement advanced analytical methods and modeling techniques to design processes, products and systems in an innovative and original way and improve them | X | ||||
| 3. Have the competency to plan, manage and monitor processes, products and systems. | X | ||||
| 4. Evaluate the data obtained from analysis of the processes, products and systems, complete limited or missing data through scientific methods, develop data driven solution approaches. | X | ||||
| 5. Develop original methods for the efficient integration of the scarce resources such as man, machine, and material, energy, capital and time to the systems and implement these. | X | ||||
| 6. Effectively utilize computer programming languages, computer software, information and communication technology to solve problems in the field of industrial engineering. | X | ||||
| 7. Report and present advanced studies, outcomes/results and the evaluations on the design, analysis, planning, monitoring and improvement of processes, products and systems. | X | ||||
| 8. Are aware of the professional responsibility, describe the technological, economic and environmental effects of the industrial engineering applications, work as an individual independently and as a team member having an understanding of the scientific ethical values, take responsibility and lead the team. | X | ||||
| 9. Are aware of the up-to-date engineering applications, follow the necessary literature for advanced researches, have the competency to reach knowledge in a foreign language, to quote and implement them. | X | ||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest