Course Detail
Course Detail
Course Description
| Course | Code | Semester | T+P (Hour) | Credit | ECTS |
|---|---|---|---|---|---|
| STOCHASTIC MODELS | IND2249060 | Spring Semester | 3+0 | 3 | 6 |
| Course Program |
| Prerequisites Courses | |
| Recommended Elective Courses |
| Language of Course | English |
| Course Level | First Cycle (Bachelor's Degree) |
| Course Type | Required |
| Course Coordinator | Assist.Prof. Engin SANSARCI |
| Name of Lecturer(s) | Assist.Prof. Engin SANSARCI |
| Assistant(s) | Teaching Assistant: Ersin Durmuşkaya ([email protected]) |
| Aim | This course aims to introduce basic stochastic models in order to deal with uncertainties in Industrial engineering problems and presents how to develop Markov models to reflect stochastic processes faced in real life situations. |
| Course Content | This course contains; Introduction to the Course,Review of Probability Theory,Conditional Probability and Conditional Expectation,Introduction to Stochastic Processes and Markov Chains,Discrete Time Markov Chains-1,Discrete Time Markov Chains-2,The Exponential Distribution and Poisson Process-1,The Exponential Distribution and Poisson Process-2,Continuous Time Markov Chains-1,Continuous Time Markov Chains-2,Queuing Systems-1,Queuing Systems-2,Queuing Systems-3,General Review. |
| Course Learning Outcomes | Teaching Methods | Assessment Methods |
| 1. Students differentiate deterministic and stochastic systems and apply fundamental concepts of probability theory to analyze uncertainty. | 16, 6, 9 | A, G |
| 2. Students implement modeling methodologies of uncertainty in industrial engineering problems. | 16, 6, 9 | A, G |
| 3. Students define the exponential distribution and its relationship with the Poisson process. | 16, 6, 9 | A, G |
| 4. Students analyze Markov Chain models. | 16, 6, 9 | A, G |
| 5. Students defıne queuing theory. | 16, 6, 9 | A, G |
| 6. The student analyzes industrial engineering problems holistically using stochastic modeling methods, develops appropriate solution approaches, and interprets the results. | 16, 6, 9 | A, G |
| Teaching Methods: | 16: Question - Answer Technique, 6: Experiential Learning, 9: Lecture Method |
| Assessment Methods: | A: Traditional Written Exam, G: Quiz |
Course Outline
| Order | Subjects | Preliminary Work |
|---|---|---|
| 1 | Introduction to the Course | |
| 2 | Review of Probability Theory | |
| 3 | Conditional Probability and Conditional Expectation | |
| 4 | Introduction to Stochastic Processes and Markov Chains | |
| 5 | Discrete Time Markov Chains-1 | |
| 6 | Discrete Time Markov Chains-2 | |
| 7 | The Exponential Distribution and Poisson Process-1 | |
| 8 | The Exponential Distribution and Poisson Process-2 | |
| 9 | Continuous Time Markov Chains-1 | |
| 10 | Continuous Time Markov Chains-2 | |
| 11 | Queuing Systems-1 | |
| 12 | Queuing Systems-2 | |
| 13 | Queuing Systems-3 | |
| 14 | General Review |
| Resources |
| Introduction to Probability Models by Sheldon Ross, Academic Press. Operations Research: Applications & Algorithms by W.L. Winston Thomson |
| Operations Research: Applications & Algorithms by W.L. Winston Thomson, ISBN: 0-534-42362-0. |
Course Contribution to Program Qualifications
| Course Contribution to Program Qualifications | |||||||
| No | Program Qualification | Contribution Level | |||||
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | Adequate knowledge in mathematics, science and engineering subjects pertaining to the relevant discipline; ability to use theoretical and applied knowledge in these areas in the solution of complex engineering problems. | X | |||||
| 2 | Ability to formulate, and solve complex engineering problems; ability to select and apply proper analysis and modeling methods for this purpose. | X | |||||
| 3 | Ability to design a complex system, process, device or product under realistic constraints and conditions, in such a way as to meet the desired result; ability to apply modern design methods for this purpose. | ||||||
| 4 | Ability to select and use modern techniques and tools needed for analyzing and solving complex problems encountered in engineering practice; ability to employ information technologies effectively. | X | |||||
| 5 | Ability to design and conduct experiments, gather data, analyze and interpret results for investigating complex engineering problems or discipline specific research questions. | ||||||
| 6 | Ability to work efficiently in intra-disciplinary and multi-disciplinary teams; ability to work individually. | ||||||
| 7 | Ability to communicate effectively, both orally and in writing; knowledge of a minimum of one foreign language; ability to write effective reports and comprehend written reports, prepare design and production reports, make effective presentations, and give and receive clear and intelligible instructions. | ||||||
| 8 | Awareness of the need for lifelong learning; ability to access information, to follow developments in science and technology, and to continue to educate him/herself. | X | |||||
| 9 | Knowledge on behavior according ethical principles, professional and ethical responsibility and standards used in engineering practices. | ||||||
| 10 | Knowledge about business life practices such as project management, risk management, and change management; awareness in entrepreneurship, innovation; knowledge about sustainable development. | ||||||
| 11 | Knowledge about the global and social effects of engineering practices on health, environment, and safety, and contemporary issues of the century reflected into the field of engineering; awareness of the legal consequences of engineering solutions. | ||||||
Assessment Methods
| Contribution Level | Absolute Evaluation | |
| Rate of Midterm Exam to Success | 30 | |
| Rate of Final Exam to Success | 70 | |
| Total | 100 | |
| ECTS / Workload Table | ||||||
| Activities | Number of | Duration(Hour) | Total Workload(Hour) | |||
| Course Hours | 14 | 3 | 42 | |||
| Guided Problem Solving | 14 | 2 | 28 | |||
| Resolution of Homework Problems and Submission as a Report | 3 | 10 | 30 | |||
| Term Project | 1 | 8 | 8 | |||
| Presentation of Project / Seminar | 0 | 0 | 0 | |||
| Quiz | 3 | 10 | 30 | |||
| Midterm Exam | 1 | 18 | 18 | |||
| General Exam | 1 | 24 | 24 | |||
| Performance Task, Maintenance Plan | 0 | 0 | 0 | |||
| Total Workload(Hour) | 180 | |||||
| Dersin AKTS Kredisi = Toplam İş Yükü (Saat)/30*=(180/30) | 6 | |||||
| ECTS of the course: 30 hours of work is counted as 1 ECTS credit. | ||||||
Detail Informations of the Course
Course Description
| Course | Code | Semester | T+P (Hour) | Credit | ECTS |
|---|---|---|---|---|---|
| STOCHASTIC MODELS | IND2249060 | Spring Semester | 3+0 | 3 | 6 |
| Course Program |
| Prerequisites Courses | |
| Recommended Elective Courses |
| Language of Course | English |
| Course Level | First Cycle (Bachelor's Degree) |
| Course Type | Required |
| Course Coordinator | Assist.Prof. Engin SANSARCI |
| Name of Lecturer(s) | Assist.Prof. Engin SANSARCI |
| Assistant(s) | Teaching Assistant: Ersin Durmuşkaya ([email protected]) |
| Aim | This course aims to introduce basic stochastic models in order to deal with uncertainties in Industrial engineering problems and presents how to develop Markov models to reflect stochastic processes faced in real life situations. |
| Course Content | This course contains; Introduction to the Course,Review of Probability Theory,Conditional Probability and Conditional Expectation,Introduction to Stochastic Processes and Markov Chains,Discrete Time Markov Chains-1,Discrete Time Markov Chains-2,The Exponential Distribution and Poisson Process-1,The Exponential Distribution and Poisson Process-2,Continuous Time Markov Chains-1,Continuous Time Markov Chains-2,Queuing Systems-1,Queuing Systems-2,Queuing Systems-3,General Review. |
| Course Learning Outcomes | Teaching Methods | Assessment Methods |
| 1. Students differentiate deterministic and stochastic systems and apply fundamental concepts of probability theory to analyze uncertainty. | 16, 6, 9 | A, G |
| 2. Students implement modeling methodologies of uncertainty in industrial engineering problems. | 16, 6, 9 | A, G |
| 3. Students define the exponential distribution and its relationship with the Poisson process. | 16, 6, 9 | A, G |
| 4. Students analyze Markov Chain models. | 16, 6, 9 | A, G |
| 5. Students defıne queuing theory. | 16, 6, 9 | A, G |
| 6. The student analyzes industrial engineering problems holistically using stochastic modeling methods, develops appropriate solution approaches, and interprets the results. | 16, 6, 9 | A, G |
| Teaching Methods: | 16: Question - Answer Technique, 6: Experiential Learning, 9: Lecture Method |
| Assessment Methods: | A: Traditional Written Exam, G: Quiz |
Course Outline
| Order | Subjects | Preliminary Work |
|---|---|---|
| 1 | Introduction to the Course | |
| 2 | Review of Probability Theory | |
| 3 | Conditional Probability and Conditional Expectation | |
| 4 | Introduction to Stochastic Processes and Markov Chains | |
| 5 | Discrete Time Markov Chains-1 | |
| 6 | Discrete Time Markov Chains-2 | |
| 7 | The Exponential Distribution and Poisson Process-1 | |
| 8 | The Exponential Distribution and Poisson Process-2 | |
| 9 | Continuous Time Markov Chains-1 | |
| 10 | Continuous Time Markov Chains-2 | |
| 11 | Queuing Systems-1 | |
| 12 | Queuing Systems-2 | |
| 13 | Queuing Systems-3 | |
| 14 | General Review |
| Resources |
| Introduction to Probability Models by Sheldon Ross, Academic Press. Operations Research: Applications & Algorithms by W.L. Winston Thomson |
| Operations Research: Applications & Algorithms by W.L. Winston Thomson, ISBN: 0-534-42362-0. |
Course Contribution to Program Qualifications
| Course Contribution to Program Qualifications | |||||||
| No | Program Qualification | Contribution Level | |||||
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | Adequate knowledge in mathematics, science and engineering subjects pertaining to the relevant discipline; ability to use theoretical and applied knowledge in these areas in the solution of complex engineering problems. | X | |||||
| 2 | Ability to formulate, and solve complex engineering problems; ability to select and apply proper analysis and modeling methods for this purpose. | X | |||||
| 3 | Ability to design a complex system, process, device or product under realistic constraints and conditions, in such a way as to meet the desired result; ability to apply modern design methods for this purpose. | ||||||
| 4 | Ability to select and use modern techniques and tools needed for analyzing and solving complex problems encountered in engineering practice; ability to employ information technologies effectively. | X | |||||
| 5 | Ability to design and conduct experiments, gather data, analyze and interpret results for investigating complex engineering problems or discipline specific research questions. | ||||||
| 6 | Ability to work efficiently in intra-disciplinary and multi-disciplinary teams; ability to work individually. | ||||||
| 7 | Ability to communicate effectively, both orally and in writing; knowledge of a minimum of one foreign language; ability to write effective reports and comprehend written reports, prepare design and production reports, make effective presentations, and give and receive clear and intelligible instructions. | ||||||
| 8 | Awareness of the need for lifelong learning; ability to access information, to follow developments in science and technology, and to continue to educate him/herself. | X | |||||
| 9 | Knowledge on behavior according ethical principles, professional and ethical responsibility and standards used in engineering practices. | ||||||
| 10 | Knowledge about business life practices such as project management, risk management, and change management; awareness in entrepreneurship, innovation; knowledge about sustainable development. | ||||||
| 11 | Knowledge about the global and social effects of engineering practices on health, environment, and safety, and contemporary issues of the century reflected into the field of engineering; awareness of the legal consequences of engineering solutions. | ||||||
Assessment Methods
| Contribution Level | Absolute Evaluation | |
| Rate of Midterm Exam to Success | 30 | |
| Rate of Final Exam to Success | 70 | |
| Total | 100 | |