IE 353 | Course Introduction and Application Information

Course Name
Optimization III-Stochastic Models
Code
Semester
Theory
(hour/week)
Application/Lab
(hour/week)
Local Credits
ECTS
IE 353
Fall/Spring
3
0
3
8

Prerequisites
  IE 251 To succeed (To get a grade of at least DD)
and IE 240 To succeed (To get a grade of at least DD)
or MATH 240 To succeed (To get a grade of at least DD)
Course Language
English
Course Type
Elective
Course Level
First Cycle
Course Coordinator
Course Lecturer(s)
Assistant(s)
Course Objectives Most systems and processes of organizations operating in almost all kinds of sectors (private/public, service/manufacturing etc.) are stochastic in nature. The objective of this course is to give the students the analytical skills and knowledge related to stochastic processes and models necessary to improve the systems and processes used in varying organizations.
Course Description The students who succeeded in this course;
  • define the sources of uncertainty with stochastic processes in the Industrial Engineering context
  • model stochastic processes using Markov chains
  • classify Markov chains
  • conduct optimization under uncertainty using Markov decision processes
  • analyze the performance of different systems using queueing theory
Course Content The main subjects of the course are the stochastic processes and their special kind called Markov chains, queueing theory, inventory theory and also possible real life applications.

 



Course Category

Core Courses
Major Area Courses
Supportive Courses
Media and Management Skills Courses
Transferable Skill Courses

 

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week Subjects Related Preparation
1 Review of probability Ross, Ch. 1, Taylor&Karlin-Ch.I
2 Conditional Probability and Random Variables Ross, Ch. 2, Taylor&Karlin-Ch.I
3 Discrete, Continuous Random Variables and Expected Values Ross, Ch. 3, Taylor&Karlin-Ch.II
4 Markov Chains Winston, Ch. 17, Hillier & Lieberman Ch. 16, Taylor&Karlin-Ch.III
5 Markov Chains Winston, Ch. 17, Hillier & Lieberman Ch. 16, Taylor&Karlin-Ch.III
6 Markov Chains Winston, Ch. 17, Hillier & Lieberman Ch. 16, Taylor&Karlin-Ch.III
7 Markov Chains Winston, Ch. 17, Hillier & Lieberman Ch. 16, Taylor&Karlin-Ch.IV
8 Markov Decision Processes Winston, Ch. 19.5, Hillier & Lieberman Ch. 19
9 Markov Decision Processes Winston, Ch. 19.5, Hillier & Lieberman Ch. 19
10 Continuous Time Markov Chains Ross, Ch. 6, Hillier & Lieberman Ch. 16.8, Taylor&Karlin-Ch.VI
11 Queueing Theory Winston, Ch. 20, Hillier & Lieberman Ch. 17
12 Queueing Theory Winston, Ch. 20, Hillier & Lieberman Ch. 17
13 Queueing Theory Winston, Ch. 20, Hillier & Lieberman Ch. 17
14 Queueing Theory Winston, Ch. 20, Hillier & Lieberman Ch. 17
15 Review of the semester
16 Final Exam

 

Course Notes/Textbooks

[1] Wayne L. Winston, Operations Research: Applications and Algorithms, (International Student Edition), 4th edition, Brooks/Cole, 2004. ISBN: 0-534-42362-0

[2] Frederick S. Hillier, Gerald J. Lieberman, Introduction to Operations Research, Tenth Edition, 2010 Mc GrawHill, ISBN: 9780071267670

[3] Sheldon Ross, Introduction to Probability Models, 11th edition, Academic Press, 2014.ISBN: 978-0124079489.

[4] Taylor, Howard M. and Karlin, Samuel. An Introduction to Stochastic Modeling, 3rd Edition, Academic Press, 1998, ISBN: 978-0-12-684887-8.

Suggested Readings/Materials

[5] V. G. Kulkarni. Modeling, Analysis, Design and Control of Stochastic Systems. Springer, 1999.

[6] Sheldon Ross. A First Course in Probability. Fifth Ed., Prentice Hall, Ltd., 1997.

 

EVALUATION SYSTEM

Semester Activities Number Weigthing
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques
2
20
Homework / Assignments
Presentation / Jury
Project
Seminar / Workshop
Oral Exams
Midterm
1
35
Final Exam
1
45
Total

Weighting of Semester Activities on the Final Grade
7
55
Weighting of End-of-Semester Activities on the Final Grade
1
45
Total

ECTS / WORKLOAD TABLE

Semester Activities Number Duration (Hours) Workload
Theoretical Course Hours
(Including exam week: 16 x total hours)
16
3
48
Laboratory / Application Hours
(Including exam week: 16 x total hours)
16
Study Hours Out of Class
16
8
Field Work
Quizzes / Studio Critiques
2
10
Homework / Assignments
Presentation / Jury
Project
Seminar / Workshop
Oral Exam
Midterms
1
20
Final Exam
1
20
    Total
236

 

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#
Program Competencies/Outcomes
* Contribution Level
1
2
3
4
5
1

To have adequate knowledge in Mathematics, Science, Computer Science and Software Engineering; to be able to use theoretical and applied information in these areas on complex engineering problems.

2

To be able to identify, define, formulate, and solve complex Software Engineering problems; to be able to select and apply proper analysis and modeling methods for this purpose.

3

To be able to design, implement, verify, validate, document, measure and maintain a complex software system, process, or product under realistic constraints and conditions, in such a way as to meet the requirements; ability to apply modern methods for this purpose.

4

To be able to devise, select, and use modern techniques and tools needed for analysis and solution of complex problems in software engineering applications; to be able to use information technologies effectively.

5

To be able to design and conduct experiments, gather data, analyze and interpret results for investigating complex Software Engineering problems.

6

To be able to work effectively in Software Engineering disciplinary and multi-disciplinary teams; to be able to work individually.

7

To be able to communicate effectively in Turkish, both orally and in writing; to be able to author and comprehend written reports, to be able to prepare design and implementation reports, to be able to present effectively, to be able to give and receive clear and comprehensible instructions.

8

To have knowledge about global and social impact of engineering practices and software applications on health, environment, and safety; to have knowledge about contemporary issues as they pertain to engineering; to be aware of the legal ramifications of Engineering and Software Engineering solutions.

9

To be aware of ethical behavior, professional and ethical responsibility; to have knowledge about standards utilized in engineering applications.

10

To have knowledge about industrial practices such as project management, risk management, and change management; to have awareness of entrepreneurship and innovation; to have knowledge about sustainable development.

11

To be able to collect data in the area of Software Engineering, and to be able to communicate with colleagues in a foreign language.

12

To be able to speak a second foreign language at a medium level of fluency efficiently.

13

To recognize the need for lifelong learning; to be able to access information, to be able to stay current with developments in science and technology; to be able to relate the knowledge accumulated throughout the human history to Software Engineering.

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest