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 | 4 | 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 Content
Koşullu olasılık ve beklenen değer Kesikli ve sürekli zaman Markov zincirleri Poisson süreci Kuyruk sistemlerinin modellenmesi – tek ve çok sunuculu kuyruklar
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 | Ability to design, conduct experiments, collect data, analyze and interpret results for the study of complex problems or discipline-specific research topics in the field of Industrial Engineering | 5 |
Course Learning Outcomes
| Upon the successful completion of this course, students will be able to: | |||
|---|---|---|---|
| No | Learning Outcomes | Outcome Relationship | Measurement Method ** |
| O1 | Stokastik süreçlerin temel kavramlarını anlar ve bu süreçleri modelleme yeteneği geliştirir. | P.5.11 | 1 |
| O2 | Stokastik karar problemlerini çözmek için uygun matematiksel yöntemleri uygular. | P.5.12 | 1 |
| O3 | Gerçek dünya senaryolarında stokastik modelleme yaparak belirsizlik altında karar verme becerisi kazanır. | P.5.13 | 1 |
| O4 | Stokastik yöneylem araştırması tekniklerini kullanarak sistem performansını analiz etmek ve iyileştirir. | 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 | Winston, W.L. (2004) Operations Research Applications and Algorithms, 4th ed., Thomson/Brooks/Cole. |
| 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 | Stokastik süreçlerin temel kavramlarını anlar ve bu süreçleri modelleme yeteneği geliştirir. | 5 |
| O2 | Stokastik karar problemlerini çözmek için uygun matematiksel yöntemleri uygular. | 5 |
| O3 | Gerçek dünya senaryolarında stokastik modelleme yaparak belirsizlik altında karar verme becerisi kazanır. | 5 |
| O4 | Stokastik yöneylem araştırması tekniklerini kullanarak sistem performansını analiz etmek ve iyileştirir. | 5 |