| Semester | Course Unit Code | Course Unit Title | T+P+L | Credit | Number of ECTS Credits |
| 5 | IE335 | STOCHASTIC MODELS | 3+0+0 | 3 | 5 |
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Language of Instruction
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English
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Level of Course Unit
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Bachelor's Degree
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Department / Program
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INDUSTRIAL ENGINEERING
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Type of Program
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Formal Education
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Type of Course Unit
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Compulsory
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Course Delivery Method
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Face To Face
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Objectives of the Course
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Introducing the basic concepts of the theory of stochastic processes.
Introduction of the most important types of stochastic processes.
To study various properties and characteristics of processes.
To equip the students to be able to model random events.
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Course Content
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The course is intended for the junior undergraduate students in Industrial Engineering. Topics to be covered include Markov chains in discrete and continuous cases, the Poisson processes and exponential distribution, and queuing theory. The course requires basic knowledge in probability theory and linear algebra. Students are expected to use and understand basic mathematical notations; select and apply an appropriate mathematical model for certain elementary probabilistic problems; and do calculations accurately.
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Course Methods and Techniques
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We will be using various tools for active learning to take place. This is also a student-driven course. It is your responsibility to participate actively in class discussions.
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Prerequisites and co-requisities
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( IE221 )
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Course Coordinator
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None
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Name of Lecturers
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Asist Prof.Dr. Rahime Şeyma Bekli seyma.bekli@agu.edu.tr
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Assistants
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None
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Work Placement(s)
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No
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Recommended or Required Reading
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Resources
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Ross, Sheldon M. Introduction to Probability Models. Academic Press, 2014.
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will be shared on canvas system
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Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"
Assessment Methods and Criteria
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In-Term Studies
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Yarıl yılSonu Sınavı/Dönem Projesinin Başarı Notuna Katkısı
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2
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%
36
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Quiz/Küçük Sınav
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3
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%
15
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Ödev
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3
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%
15
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Final examination
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1
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%
30
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Diğer (Staj vb.)
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1
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%
4
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Total
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10
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%
100
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ECTS Allocated Based on Student Workload
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Activities
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Total Work Load
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Sınıf İçi Aktivitesi
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14
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3
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42
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Kişisel Çalışma
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14
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1
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14
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Ders dışı çalışma
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14
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3
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42
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Yüz Yüze Ders
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3
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14
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42
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Final Sınavı
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1
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3
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3
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Total Work Load
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Number of ECTS Credits 5
143
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Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
| No | Learning Outcomes |
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1
| Model uncertainty using basic stochastic processes. |
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2
| Set up and analyze Markov chains. |
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3
| Develop Markovian models for IE applications. |
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4
| Derive and apply main formulas for some properties (e.g., stationary probabilities, average waiting and system time, expected number of customers in the queue) of queuing systems. |
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5
| Develop appropriate Markov decision processes to solve problems under uncertainty and risk. |
Weekly Detailed Course Contents
| Week | Topics | Study Materials | Materials |
| 1 |
Review of Probability
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| 2 |
Review of Probability
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| 3 |
Poisson processes and exponential distribution
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| 4 |
Poisson processes and exponential distribution
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| 5 |
Discrete-Time Markov Chain
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| 6 |
Lecture Free Week
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| 7 |
Fall Break
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| 8 |
Discrete-Time Markov Chain
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| 9 |
Discrete-Time Markov Chain
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| 10 |
Continuous-Time Markov Chain
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| 11 |
Continuous-Time Markov Chain
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| 12 |
Queuing Theory
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| 13 |
Queuing Theory
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| 14 |
Queuing Theory
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| 15 |
Discrete -Time Markov Decision Processes
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| 16 |
Final Exam, Term Project
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Recommended Optional Programme Components
Contribution of Learning Outcomes to Programme Outcomes
Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant
https://sis.agu.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=71666&lang=en