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
Oral Exams
Midterm
1
30
Final 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
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
15
4
Field Work
Quizzes / Studio Critiques
Homework / Assignments
3
7
Presentation / Jury
Project
1
20
Seminar / Workshop
Oral Exam
Midterms
1
8
Final Exam
1
13
    Total
170

 

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