| Course Unit Title | Course Unit Code | Type of Course Unit | Level of Course Unit | Year of Study | Semester | ECTS Credits |
|---|---|---|---|---|---|---|
| Stochastic Models | MEN316 | Elective | Bachelor's degree | 3 | Spring | 5 |
Name of Lecturer(s)
Assistant Prof. Dr. Yıldız ŞAHİN
Learning Outcomes of the Course Unit
1) Students will have a concept of stochastic processes and markov processes
2) Students should understand discrete time Markov chains, the transient and steady-state analysis.
3) Students will be able to understand the concept of exponential distribution, continuous timely birth-death chains
4) Students will be able to conceptualize and implement Queuing models, Markovian queues, Queuing Theory and queuing approaches.
Program Competencies-Learning Outcomes Relation
| Program Competencies | ||||||||||||
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | ||
| Learning Outcomes | ||||||||||||
| 1 | High | High | High | Low | High | High | Middle | Low | High | Low | No relation | |
| 2 | High | High | High | Low | High | High | Middle | Low | High | Low | No relation | |
| 3 | High | High | High | Low | High | High | Middle | Low | High | Low | No relation | |
| 4 | High | High | High | Middle | High | High | Middle | Middle | High | Low | No relation | |
Mode of Delivery
Face to Face
Prerequisites and Co-Requisites
None
Recommended Optional Programme Components
Not Required
Course Contents
Stochastic processes, Markov Processes, discrete event Markov chains, transiet and steady state analysis, exponential distribution, continious event birth-death chains, queuing models, Markovian queues, Queue Theory and queue approximations
Weekly Schedule
1) Stochastic process2) Stochastic process
3) Markov Processes
4) Discrete-time Markov chains
5) Discrete-time Markov chains
6) Transient ve steady-state analysis
7) Exponential distribution
8) Midterm exam
9) Queue models
10) Queue models
11) Markovian queues
12) Queue Theory and queuing approaches
13) Queue Theory and queuing approaches
14) Queue Theory and queuing approaches
15) Queue Theory and queuing approaches
16) Final exam
Recommended or Required Reading
1- - Winston, W. (2003). Operations Research : Applications and Algorithms, Duxbury Press.
2- - Ross, S. M. (2000). Introduction to Probability Models, Seventh Edition, Academic Press.
3- - Hillier, Frederick S. and Gerald G. Lieberman (1990), Introduction to Stochastic Models in Operations Research, New York: McGraw-Hill Inc.
4- - Taha, H.A. (2002). Operations Research: An Introduction, Prentice Hall.
5- - Hillier, F.S., Lieberman, G.J. (2002). Introduction to Operations Research, McGraw-Hill.
Planned Learning Activities and Teaching Methods
1) Question-Answer
2) Drill and Practice
3) Modelling
4) Self Study
5) Problem Solving
Assessment Methods and Criteria
Contribution of Midterm Examination to Course Grade |
40% |
|---|---|
Contribution of Final Examination to Course Grade |
60% |
Total |
100% |
Language of Instruction
Turkish
Work Placement(s)
Not Required