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Course Detail

Course Description

CourseCodeSemesterT+P (Hour)CreditECTS
STOCHASTIC MODELS-Spring Semester3+036
Course Program
Prerequisites Courses
Recommended Elective Courses
Language of CourseEnglish
Course LevelFirst Cycle (Bachelor's Degree)
Course TypeRequired
Course CoordinatorAssoc.Prof. Yasin GÖÇGÜN
Name of Lecturer(s)Assoc.Prof. Yasin GÖÇGÜN
Assistant(s)Teaching Assistant: Ersin Durmuşkaya ([email protected])
AimThis 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 ContentThis 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.
Dersin Öğrenme KazanımlarıTeaching MethodsAssessment Methods
Students differentiate deterministic and stochastic cases.16, 6, 9A, E
Students implement modeling methodologies of uncertainty in industrial engineering problems.16, 6, 9A, E
Students define the exponential distribution and its relationship with the Poisson process. 16, 6, 9A, E
Students analyze Markov Chain models.16, 6, 9A, E
Students defıne queuing theory.16, 6, 9A, E
Teaching Methods:16: Question - Answer Technique, 6: Experiential Learning, 9: Lecture Method
Assessment Methods:A: Traditional Written Exam, E: Homework

Course Outline

OrderSubjectsPreliminary Work
1Introduction to the Course
2Review of Probability Theory
3Conditional Probability and Conditional Expectation
4Introduction to Stochastic Processes and Markov Chains
5Discrete Time Markov Chains-1
6Discrete Time Markov Chains-2
7The Exponential Distribution and Poisson Process-1
8The Exponential Distribution and Poisson Process-2
9Continuous Time Markov Chains-1
10Continuous Time Markov Chains-2
11Queuing Systems-1
12Queuing Systems-2
13Queuing Systems-3
14General 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
NoProgram QualificationContribution Level
12345
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 LevelAbsolute Evaluation
Rate of Midterm Exam to Success 30
Rate of Final Exam to Success 70
Total 100
ECTS / Workload Table
ActivitiesNumber ofDuration(Hour)Total Workload(Hour)
Course Hours14342
Guided Problem Solving14228
Resolution of Homework Problems and Submission as a Report31030
Term Project188
Presentation of Project / Seminar000
Quiz31030
Midterm Exam11818
General Exam12424
Performance Task, Maintenance Plan000
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

CourseCodeSemesterT+P (Hour)CreditECTS
STOCHASTIC MODELS-Spring Semester3+036
Course Program
Prerequisites Courses
Recommended Elective Courses
Language of CourseEnglish
Course LevelFirst Cycle (Bachelor's Degree)
Course TypeRequired
Course CoordinatorAssoc.Prof. Yasin GÖÇGÜN
Name of Lecturer(s)Assoc.Prof. Yasin GÖÇGÜN
Assistant(s)Teaching Assistant: Ersin Durmuşkaya ([email protected])
AimThis 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 ContentThis 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.
Dersin Öğrenme KazanımlarıTeaching MethodsAssessment Methods
Students differentiate deterministic and stochastic cases.16, 6, 9A, E
Students implement modeling methodologies of uncertainty in industrial engineering problems.16, 6, 9A, E
Students define the exponential distribution and its relationship with the Poisson process. 16, 6, 9A, E
Students analyze Markov Chain models.16, 6, 9A, E
Students defıne queuing theory.16, 6, 9A, E
Teaching Methods:16: Question - Answer Technique, 6: Experiential Learning, 9: Lecture Method
Assessment Methods:A: Traditional Written Exam, E: Homework

Course Outline

OrderSubjectsPreliminary Work
1Introduction to the Course
2Review of Probability Theory
3Conditional Probability and Conditional Expectation
4Introduction to Stochastic Processes and Markov Chains
5Discrete Time Markov Chains-1
6Discrete Time Markov Chains-2
7The Exponential Distribution and Poisson Process-1
8The Exponential Distribution and Poisson Process-2
9Continuous Time Markov Chains-1
10Continuous Time Markov Chains-2
11Queuing Systems-1
12Queuing Systems-2
13Queuing Systems-3
14General 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
NoProgram QualificationContribution Level
12345
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 LevelAbsolute Evaluation
Rate of Midterm Exam to Success 30
Rate of Final Exam to Success 70
Total 100

Numerical Data

Student Success

Ekleme Tarihi: 09/10/2023 - 10:42Son Güncelleme Tarihi: 09/10/2023 - 10:43