EMÜ668 - QUEUEING SYSTEMS
| Course Name | Code | Semester | Theory (hours/week) |
Application (hours/week) |
Credit | ECTS |
|---|---|---|---|---|---|---|
| QUEUEING SYSTEMS | EMÜ668 | Any Semester/Year | 3 | 0 | 3 | 10 |
| Prequisites | ||||||
| Course language | Turkish | |||||
| Course type | Elective | |||||
| Mode of Delivery | Face-to-Face | |||||
| Learning and teaching strategies | Lecture Question and Answer Problem Solving Project Design/Management Other: Lecture, question and answer, problem solving, project design/management, individual study. | |||||
| Instructor (s) | To be determined by the department | |||||
| Course objective | The objective of this course is to develop students? skills to build mathematical models for queueing systems and compute system performance measures to solve decision making problems in queueing systems | |||||
| Learning outcomes |
| |||||
| Course Content | Characteristics of queueing systems Poisson processes Markov chains Markovian queueing systems General arrival or service patterns Queueing networks, series and cyclic queues | |||||
| References | Shortle, J.F., Thompson, J.M., Gross, D., Harris, C.M., (2018), Fundamentals of Queueing Theory, Wiley Interscience. Kleinrock, L., (1975), Queueing Systems, Vol. I , Wiley Interscience Pinsky, M.A., Karlin, S., (2010), An Introduction to Stochastic Modeling, Academic Press Up-to-date research articles about queueing systems and applications | |||||
Course outline weekly
| Weeks | Topics |
|---|---|
| Week 1 | Characteristics of queueing systems |
| Week 2 | Exponential distribution, Poisson processes |
| Week 3 | Discrete-time Markov chains |
| Week 4 | Continuous-time Markov chains, birth-and-death processes |
| Week 5 | Single-server and multiserver Markovian queues |
| Week 6 | Queues with truncation, Erlang's loss formula, queues with unlimited service |
| Week 7 | Finite source queues |
| Week 8 | State dependent service, queues with impatience, transient behavior |
| Week 9 | Midterm exam |
| Week 10 | Bulk input and bulk service Markovian queues |
| Week 11 | Erlang models and priority queues |
| Week 12 | Series queues and open Jackson networks |
| Week 13 | Closed Jackson networks, cyclic queues and non-Jackson networks |
| Week 14 | General arrival or service patterns |
| Week 15 | Study for the Final Exam |
| Week 16 | Final exam |
Assesment methods
| Course activities | Number | Percentage |
|---|---|---|
| Attendance | 0 | 0 |
| Laboratory | 0 | 0 |
| Application | 0 | 0 |
| Field activities | 0 | 0 |
| Specific practical training | 0 | 0 |
| Assignments | 4 | 10 |
| Presentation | 1 | 3 |
| Project | 1 | 12 |
| Seminar | 0 | 0 |
| Midterms | 1 | 25 |
| Final exam | 1 | 50 |
| Total | 100 | |
| Percentage of semester activities contributing grade succes | 7 | 50 |
| Percentage of final exam contributing grade succes | 1 | 50 |
| Total | 100 | |
WORKLOAD AND ECTS CALCULATION
| Activities | Number | Duration (hour) | Total Work Load |
|---|---|---|---|
| Course Duration (x14) | 14 | 3 | 42 |
| Laboratory | 0 | 0 | 0 |
| Application | 0 | 0 | 0 |
| Specific practical training | 1 | 10 | 10 |
| Field activities | 0 | 0 | 0 |
| Study Hours Out of Class (Preliminary work, reinforcement, ect) | 13 | 6 | 78 |
| Presentation / Seminar Preparation | 1 | 10 | 10 |
| Project | 1 | 40 | 40 |
| Homework assignment | 4 | 15 | 60 |
| Midterms (Study duration) | 1 | 30 | 30 |
| Final Exam (Study duration) | 1 | 40 | 40 |
| Total Workload | 36 | 154 | 310 |
Matrix Of The Course Learning Outcomes Versus Program Outcomes
| D.9. Key Learning Outcomes | Contrubition level* | ||||
|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |
| 1. Reach the necessary knowledge and methods in engineering within the scope of advanced industrial engineering studies through scientific research and evaluate knowledge and methods and implement them. | X | ||||
| 2. Implement advanced analytical methods and modeling techniques to design processes, products and systems in an innovative and original way and improve them | X | ||||
| 3. Have the competency to plan, manage and monitor processes, products and systems. | X | ||||
| 4. Evaluate the data obtained from analysis of the processes, products and systems, complete limited or missing data through scientific methods, develop data driven solution approaches. | X | ||||
| 5. Develop original methods for the efficient integration of the scarce resources such as man, machine, and material, energy, capital and time to the systems and implement these. | X | ||||
| 6. Effectively utilize computer programming languages, computer software, information and communication technology to solve problems in the field of industrial engineering. | X | ||||
| 7. Report and present advanced studies, outcomes/results and the evaluations on the design, analysis, planning, monitoring and improvement of processes, products and systems. | X | ||||
| 8. Are aware of the professional responsibility, describe the technological, economic and environmental effects of the industrial engineering applications, work as an individual independently and as a team member having an understanding of the scientific ethical values, take responsibility and lead the team. | X | ||||
| 9. Are aware of the up-to-date engineering applications, follow the necessary literature for advanced researches, have the competency to reach knowledge in a foreign language, to quote and implement them. | X | ||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest