Industrial Engineering
Course Details
KTO KARATAY UNIVERSITY
Mühendislik ve Doğa Bilimleri Fakültesi
Programme of Industrial Engineering
Course Details
Mühendislik ve Doğa Bilimleri Fakültesi
Programme of Industrial Engineering
Course Details
| Course Code | Course Name | Year | Period | Semester | T+A+L | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| 15271724 | Stochastic Operations Research | 2025 | Autumn | 7 | 3+0+0 | 0 | 5 |
| Course Type | Elective |
| Course Cycle | Bachelor's (First Cycle) (TQF-HE: Level 6 / QF-EHEA: Level 1 / EQF-LLL: Level 6) |
| Course Language | Turkish |
| Methods and Techniques | - |
| Mode of Delivery | Face to Face |
| Prerequisites | - |
| Coordinator | Prof. Murat DARÇIN |
| Instructor(s) | Asst. Prof. Esra BOZ |
| Instructor Assistant(s) | - |
Course Instructor(s)
| Name and Surname | Room | E-Mail Address | Internal | Meeting Hours |
|---|---|---|---|---|
| Asst. Prof. Esra BOZ | A-306 | [email protected] | 7677 |
Course Content
Conditional probability and expected value; Discrete and continuous time; Markov chains; Poisson process; Modeling of queuing systems – single and multi-server queues.
Objectives of the Course
In this course, students will be able to model uncertainties in engineering problems, construct Markov chain models in discrete and continuous time, and apply solution methods by formulating finite/infinite term stochastic optimization problems.
Contribution of the Course to Field Teaching
| Basic Vocational Courses | |
| Specialization / Field Courses | X |
| Support Courses | |
| Transferable Skills Courses | |
| Humanities, Communication and Management Skills Courses |
Relationships between Course Learning Outcomes and Program Outcomes
| Relationship Levels | ||||
| Lowest | Low | Medium | High | Highest |
| 1 | 2 | 3 | 4 | 5 |
| # | Program Learning Outcomes | Level |
|---|---|---|
| P5 | The ability to use research methods, including literature review, experimental design, experiment execution, data collection, analysis, and interpretation of results, to investigate complex industrial engineering problems. | 5 |
Course Learning Outcomes
| Upon the successful completion of this course, students will be able to: | |||
|---|---|---|---|
| No | Learning Outcomes | Outcome Relationship | Measurement Method ** |
| O1 | Understand the basic concepts of stochastic processes and develop the ability to model these processes. | P.5.11 | 1 |
| O2 | Apply appropriate mathematical methods to solve stochastic decision problems. | P.5.12 | 1 |
| O3 | Develop the ability to make decisions under uncertainty by performing stochastic modeling in real-world scenarios. | P.5.13 | 1 |
| O4 | Analyze and improve system performance using stochastic operations research techniques. | P.5.14 | 1 |
| ** Written Exam: 1, Oral Exam: 2, Homework: 3, Lab./Exam: 4, Seminar/Presentation: 5, Term Paper: 6, Application: 7 | |||
Weekly Detailed Course Contents
| Week | Topics |
|---|---|
| 1 | Conditional probability and expected value |
| 2 | Conditional probability and expected value |
| 3 | Discrete time Markov chains |
| 4 | Discrete time Markov chains |
| 5 | Discrete time Markov chains |
| 6 | Discrete time Markov chains |
| 7 | Kesikli zaman Markov zincirleri |
| 8 | Midterm exam |
| 9 | Probabilistic dynamic programming, Markov decision processes |
| 10 | Markov decision processes |
| 11 | Poisson process |
| 12 | Poisson process, continuous time Markov chains |
| 13 | Continuous time Markov chains, queuing theory |
| 14 | Queuing theory |
| 15 | Preparation for the final exam |
Textbook or Material
| Resources | Ross, M.S. (1993) Introduction to Probability Models, 5th ed., Academic Press. |
| Winston, W.L. (2004) Operations Research Applications and Algorithms, 4th ed., Thomson/Brooks/Cole. |
Evaluation Method and Passing Criteria
| In-Term Studies | Quantity | Percentage |
|---|---|---|
| Attendance | - | - |
| Laboratory | - | - |
| Practice | - | - |
| Field Study | - | - |
| Course Specific Internship (If Any) | - | - |
| Homework | - | - |
| Presentation | - | - |
| Projects | - | - |
| Seminar | - | - |
| Quiz | - | - |
| Listening | - | - |
| Midterms | 1 | 40 (%) |
| Final Exam | 1 | 60 (%) |
| Total | 100 (%) | |
ECTS / Working Load Table
| Quantity | Duration | Total Work Load | |
|---|---|---|---|
| Course Week Number and Time | 14 | 3 | 42 |
| Out-of-Class Study Time (Pre-study, Library, Reinforcement) | 14 | 3 | 42 |
| Midterms | 1 | 30 | 30 |
| Quiz | 0 | 0 | 0 |
| Homework | 0 | 0 | 0 |
| Practice | 0 | 0 | 0 |
| Laboratory | 0 | 0 | 0 |
| Project | 0 | 0 | 0 |
| Workshop | 0 | 0 | 0 |
| Presentation/Seminar Preparation | 0 | 0 | 0 |
| Fieldwork | 0 | 0 | 0 |
| Final Exam | 1 | 36 | 36 |
| Other | 0 | 0 | 0 |
| Total Work Load: | 150 | ||
| Total Work Load / 30 | 5 | ||
| Course ECTS Credits: | 5 | ||
Course - Learning Outcomes Matrix
| Relationship Levels | ||||
| Lowest | Low | Medium | High | Highest |
| 1 | 2 | 3 | 4 | 5 |
| # | Learning Outcomes | P5 |
|---|---|---|
| O1 | Understand the basic concepts of stochastic processes and develop the ability to model these processes. | 5 |
| O2 | Apply appropriate mathematical methods to solve stochastic decision problems. | 5 |
| O3 | Develop the ability to make decisions under uncertainty by performing stochastic modeling in real-world scenarios. | 5 |
| O4 | Analyze and improve system performance using stochastic operations research techniques. | 5 |