INDUSTRIAL ENGINEERING (ENGLISH, PHD)
PhD	TR-NQF-HE: Level 8	QF-EHEA: Third Cycle	EQF-LLL: Level 8

Course Introduction and Application Information

Course Code	Course Name	Semester	Theoretical	Practical	Credit	ECTS
INE6105	Stochastic Models	Spring	3	0	3	9

Basic information

Language of instruction:	English
Type of course:	Must Course
Course Level:
Mode of Delivery:	Face to face
Course Coordinator :	Dr. Öğr. Üyesi ETHEM ÇANAKOĞLU
Course Lecturer(s):	Dr. Öğr. Üyesi ETHEM ÇANAKOĞLU
Recommended Optional Program Components:	In the INE undergraduate curriculum, there is a must course named “MATH 3024 Probability and Random Variables” where the fundamental concepts of probability theory and probability distributions are given. In the INE master curriculum, there is a must course named “INE5110 Probabilistic Models and Applications” where the basic and fundamental knowledge of stochastic processes, their types and characteristics and application areas are given. This course will follow up INE5110 and will cover more advanced topics in stochastic models.
Course Objectives:	The objective of this course is to develop and extend the knowledge of mathematical techniques underlying the application of probability theory to well known engineering and operations research problems. This PhD level course is intended to cover advanced stochastic models such as Markov processes, Poisson and renewal processes, queueing systems, reliability theory, Brownian motion and stationary processes.

Learning Outcomes

The students who have succeeded in this course;
I. Identify the nature of stochastic models.
II. Develop a model for a probabilistic real-life problem.
III. Solve a constructed stochastic model analytically.
IV. Analyze stochastic processes in real-life.

Course Content

This course is a follow up course on stochastic processes. The predecessor, “IE5110 Probabilistic Models and Applications” is offered in the Industrial Engineering master curriculum as a must course where the basic and fundamental knowledge of stochastic processes are given. This course is also offered as a must course of Industrial Engineering PhD curriculum and is intended to focus on more advanced topics in stochastic models. After gaining knowledge of these topics, the students will be able to explore research and to do PhD theses related with both the theory and applications of the stochastic processes covered in this course.

Weekly Detailed Course Contents

Week	Subject	Related Preparation
1)	Introduction and Review of Basic Processes
2)	Stochastic Processes
3)	Markov Chains
4)	Markov Processes
5)	Markov Decision Processes
6)	Markov Decision Processes
7)	Midterm
8)	Introduction to stochastic programming
9)	Introduction to stochastic programming
10)	Formulating the deterministic equivalent of stochastic programs
11)	İki Aşamalı Problem
12)	Introduction to Reinforcement Learning
13)	Monte Carlo Control
14)	Presentations

Sources

Course Notes / Textbooks:	Introduction to Probability Models, 10th ed. Sheldon M. Ross. Academic Press, 2010 978-0-12-375686-2 Introduction to Stochastic Processes, Erhan Cinlar, Dover Publications; Reprint edition 2013. 978-0486497976
References:	Stochastic Processes, 2nd ed. Sheldon M. Ross. Wiley, 1996 0-471-12062-6

Evaluation System

Semester Requirements	Number of Activities	Level of Contribution
Attendance	10	% 5
Quizzes	2	% 20
Homework Assignments	5	% 15
Midterms	1	% 25
Final	1	% 35
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 65
PERCENTAGE OF FINAL WORK		% 35
Total		% 100

ECTS / Workload Table

Activities	Number of Activities	Workload
Course Hours	13	37
Study Hours Out of Class	15	104
Homework Assignments	5	40
Quizzes	2	2
Midterms	1	3
Final	1	3
Total Workload		189

Contribution of Learning Outcomes to Programme Outcomes

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

	Program Outcomes	Level of Contribution
1)	To understand and implement areas that are related with basic sciences, mathematics, and industrial engineering at a high level.	4
2)	To have expanded and deeper knowledge in the related field including the most recent developments.	2
3)	To use and evaluate knowledge with a systematic approach.	3
4)	To have high level proficiency of necessary methods and skills to reach the latest knowledge in the field and to understand the knowledge for making research studies.	1
5)	To make a comprehensive study innovating science and technology, developing new scientific method or technological product/process, implementing a known method to a new field.
6)	To be able to detect, design, implement, and finalize an original independent research process; to manage this process
7)	To be able to contribute science and technology by publishing outcomes of academic studies in reputable scholarly environments.
8)	To be able to evaluate scientific, technological, social and cultural developments, and to transfer these developments to society with scientific objectivity and ethical responsibility.
9)	To be able to conduct critical analysis, synthesis and evaluation of thoughts and developments in focusing field.
10)	To be Able to communicate and discuss orally, in written and visually with peers by using a foreign language at least at a level of European Language Portfolio C1 General Level.
11)	To be able to conduct functional interaction to solve the problems related to the field by using the strategic decision making processes	1
12)	To have effective and efficient management capabilities