Stochastic Models (IE324) Course Detail

Course Name	Course Code	Season	Lecture Hours	Application Hours	Lab Hours	Credit	ECTS
Stochastic Models	IE324	6. Semester	3	0	0	3	6

Pre-requisite Course(s)
(IE201 veya MATH392)

Course Language	English
Course Type	Compulsory Departmental Courses
Course Level	Bachelor’s Degree (First Cycle)
Mode of Delivery	Face To Face
Learning and Teaching Strategies	Lecture, Question and Answer, Problem Solving.
Course Coordinator
Course Lecturer(s)	Asst. Prof. Dr. Kamil Demirberk ÜNLÜ
Course Assistants
Course Objectives	To prepare the student to model and analyze complex systems through the application of probabilistic techniques such as Markov Chains, and queuing analysis.
Course Learning Outcomes	The students who succeeded in this course; Will be able to develop skills in building stochastic models using Markov chains Will be able to develop skills in building stochastic models using Poisson Process. Will be able to develop an understanding of queuing systems under different configurations.
Course Content	The definition and classification of stochastic processes, Markov chains, queueing systems, stochastic inventory models.

Weekly Subjects and Releated Preparation Studies

Week	Subjects	Preparation
1	The concepts of stochastic event, process and system	[1] Chapter 1
2	Review of Probability	[1] Chapter 1,2
3	Definition and classification of stochastic processes	[1] Chapter 3
4	Markov chains: Definitions	[1] Chapter 3
5	Markov chains: Problem Formulation	[1] Chapter 3
6	Markov chains: Applications in inventory models	[1] Chapter 3
7	Poisson process	[1] Chapter 5
8	Continuous time Markov chains	[1] Chapter 6
9	Midterm
10	Birth and Death processes	[1] Chapter 6
11	Queueing systems: Modeling	[2] Chapter 8
12	Queueing systems: Analysis	[2] Chapter 9
13	Simulation of stochastic processes	[2] Chapter 11
14	Stochastic optimization models	[1] Chapter 4
15	Final Examination Period
16	Final Examination Period

Sources

Course Book	1. An introduction to Stochastic Modeling, Pinsky, Mark, and Samuel Karlin, Acadamic Press.
Other Sources	2. Introduction to Probability Models, Sheldon M. Ross, Academic Press.

Evaluation System

Requirements	Number	Percentage of Grade
Attendance/Participation	-	-
Laboratory	-	-
Application	-	-
Field Work	-	-
Special Course Internship	-	-
Quizzes/Studio Critics	-	-
Homework Assignments	-	-
Presentation	-	-
Project	-	-
Report	-	-
Seminar	-	-
Midterms Exams/Midterms Jury	1	40
Final Exam/Final Jury	1	60
Toplam	2	100

Percentage of Semester Work	40
Percentage of Final Work	60
Total	100

Course Category

Core Courses	X
Major Area Courses
Supportive Courses
Media and Managment Skills Courses
Transferable Skill Courses

The Relation Between Course Learning Competencies and Program Qualifications

#	Program Qualifications / Competencies	Level of Contribution
#	Program Qualifications / Competencies	1	2	3	4	5
1	Gains adequate knowledge in mathematics, science, and relevant engineering disciplines and acquires the ability to use theoretical and applied knowledge in these fields to solve complex engineering problems.				X
2	Gains the ability to identify, formulate, and solve complex engineering problems and the ability to select and apply appropriate analysis and modeling methods for this purpose.				X
3	Gains the ability to design a complex system, process, device, or product under realistic constraints and conditions to meet specific requirements and to apply modern design methods for this purpose.
4	Gains the ability to select and use modern techniques and tools necessary for the analysis and solution of complex engineering problems encountered in industrial engineering applications and the ability to use information technologies effectively.
5	Gains the ability to design experiments, conduct experiments, collect data, analyze results, and interpret findings for investigating complex engineering problems or discipline specific research questions.
6	Gains the ability to work effectively in intra-disciplinary and multi-disciplinary teams and the ability to work individually.
7	Gains the ability to communicate effectively in written and oral form, acquires proficiency in at least one foreign language, the ability to write effective reports and understand written reports, prepare design and production reports, make effective presentations, and give and receive clear and intelligible instructions.
8	Gains awareness of the need for lifelong learning and the ability to access information, follow developments in science and technology, and to continue to educate him/herself.
9	Gains knowledge about behaviour in accordance with ethical principles, professional and ethical responsibility and standards used in industrial engineering applications
10	Gains knowledge about business practices such as project management, risk management, and change management and develops awareness of entrepreneurship, innovation, and sustainable development.
11	Gains knowledge about the global and social effects of industrial 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.
12	Gains skills in the design, development, implementation, and improvement of integrated systems involving human, material, information, equipment, and energy.
13	Gains knowledge about appropriate analytical and experimental methods, as well as computational methods, for ensuring system integration.

ECTS/Workload Table

Activities	Number	Duration (Hours)	Total Workload
Course Hours (Including Exam Week: 16 x Total Hours)	16	3	48
Laboratory
Application
Special Course Internship
Field Work
Study Hours Out of Class
Presentation/Seminar Prepration
Project
Report
Homework Assignments
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury	1	40	40
Prepration of Final Exams/Final Jury	1	62	62
Total Workload			150