IE 339 | Course Introduction and Application Information

Course Name
Queueing Systems
Code
Semester
Theory
(hour/week)
Application/Lab
(hour/week)
Local Credits
ECTS
IE 339
Fall/Spring
3
0
3
6

Prerequisites
  IE 353 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 The purpose of this course is to introduce students to a general framework for modeling queueing systems and to the basic methodologies used for their analysis.
Course Description The students who succeeded in this course;
  • Will be able to explain the framework for modelling and analyzing queueing systems
  • Will be able to define the stochastic processes that are used in the analyses of queueing systems
  • Will be able to explain the available analytical models for queueing systems
  • Will be able to relate a system under consideration with known queueuing models
  • Will be able to use queueing models for performance analysis of service and production systems
Course Content The purpose of this course is to introduce students to a general framework for modeling queueing systems and to the basic methodologies used for their analysis. Since queueing phenomenon is in general due to randomness, the course requires extensive use of probability theory. The course will encompass the stochastic processes necessary for analyzing queueing systems. At the end the course, the students are supposed to be acquainted with the available analytical models for queueing systems and to be able to use them for performance analysis of service and production systems.

 



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 Characteristics of Queueing Systems Ch 1 D. Gross, CM. Harris, Queueing Theory, Wiley, 2009.
2 Performance Evaluation Concepts Ch 1 D. Gross, CM. Harris, Queueing Theory, Wiley, 2009.
3 Poisson Process and Exponential Distribution Ch 2 D. Gross, CM. Harris, Queueing Theory, Wiley, 2009.
4 Markov Chains Ch 2 D. Gross, CM. Harris, Queueing Theory, Wiley, 2009.
5 Simple Markovian BirthDeath Queueing Models Ch 3 D. Gross, CM. Harris, Queueing Theory, Wiley, 2009.
6 Simple Markovian BirthDeath Queueing Models Ch 3 D. Gross, CM. Harris, Queueing Theory, Wiley, 2009.
7 Review and Midterm Exam
8 Advanced Markovian Queueing Models Ch 4 D. Gross, CM. Harris, Queueing Theory, Wiley, 2009.
9 Advanced Markovian Queueing Models Ch 4 D. Gross, CM. Harris, Queueing Theory, Wiley, 2009.
10 Queueing Networks Ch 5 D. Gross, CM. Harris, Queueing Theory, Wiley, 2009.
11 Queueing Networks Ch 5 D. Gross, CM. Harris, Queueing Theory, Wiley, 2009.
12 General Distribution Models Ch 6 D. Gross, CM. Harris, Queueing Theory, Wiley, 2009.
13 General Distribution Models Ch 6 D. Gross, CM. Harris, Queueing Theory, Wiley, 2009.
14 Advanced Topics Ch 7 D. Gross, CM. Harris, Queueing Theory, Wiley, 2009.
15 General review and evaluation
16 Review of the Semester  

 

Course Notes/Textbooks D. Gross, CM. Harris, Queueing Theory, Wiley, 2009.
Suggested Readings/Materials

 

EVALUATION SYSTEM

Semester Activities Number Weigthing
Participation
1
10
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Homework / Assignments
3
10
Presentation / Jury
Project
1
20
Seminar / Workshop
Portfolios
Midterms / Oral Exams
1
30
Final / Oral Exam
1
30
Total

Weighting of Semester Activities on the Final Grade
70
Weighting of End-of-Semester Activities on the Final Grade
30
Total

ECTS / WORKLOAD TABLE

Semester Activities Number Duration (Hours) Workload
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
15
4
Field Work
Quizzes / Studio Critiques
Homework / Assignments
3
7
Presentation / Jury
Project
1
20
Seminar / Workshop
Portfolios
Midterms / Oral Exams
1
8
Final / Oral Exam
1
13
    Total
170

 

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#
Program Competencies/Outcomes
* Contribution Level
1
2
3
4
5
1 Adequate knowledge in Mathematics, Science and Software Engineering; ability to use theoretical and applied information in these areas to model and solve Software Engineering problems
2 Ability to identify, define, formulate, and solve complex Software Engineering problems; ability to select and apply proper analysis and modeling methods for this purpose
3 Ability to design, implement, verify, validate, measure and maintain a complex software system, process or product under realistic constraints and conditions, in such a way as to meet the desired result; ability to apply modern methods for this purpose
4 Ability to devise, select, and use modern techniques and tools needed for Software Engineering practice
5 Ability to design and conduct experiments, gather data, analyze and interpret results for investigating Software Engineering problems
6 Ability to work efficiently in Software Engineering disciplinary and multi-disciplinary teams; ability to work individually
7 Ability to communicate effectively in Turkish, both orally and in writing; knowledge of a minimum of two foreign languages
8 Recognition of the need for lifelong learning; ability to access information, to follow developments in science and technology, and to continue to educate him/herself
9 Awareness of professional and ethical responsibility
10 Information about business life practices such as project management, risk management, and change management; awareness of entrepreneurship, innovation, and sustainable development
11 Knowledge about contemporary issues and the global and societal effects of engineering practices on health, environment, and safety; awareness of the legal consequences of Software Engineering solutions

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