EMÜ657 - SIMULATION ANALYSIS and APPLICATIONS
| Course Name | Code | Semester | Theory (hours/week) |
Application (hours/week) |
Credit | ECTS |
|---|---|---|---|---|---|---|
| SIMULATION ANALYSIS and APPLICATIONS | EMÜ657 | 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 Drill and Practice Problem Solving Other: Lecture, question and answer, problem solving, drill and practice, individual work | |||||
| Instructor (s) | To be determined by the department | |||||
| Course objective | Developing students' skills to build simulation models of processes and systems and to analyze the inputs and outputs of simulation models | |||||
| Learning outcomes |
| |||||
| Course Content | Simulation modeling concepts and discrete-event simulation Building simulation models Random number and random variate generation Selection of probability distributions for model inputs Validation and verification Output analysis Ranking and selection of alternative systems Variance reduction techniques | |||||
| References | Law, A.M. (2007) Simulation Modeling and Analysis, 4th ed. McGraw Hill. Banks, J., Carson. J.S., Nelson, B.L. and Nicole,D.M. (2010) Discrete Event System Simulation, 5th ed. Prentice Hall. Up-to-date research articles about simulation analysis and applications | |||||
Course outline weekly
| Weeks | Topics |
|---|---|
| Week 1 | Basic simulation modeling |
| Week 2 | Basic simulation modeling |
| Week 3 | Generating random numbers |
| Week 4 | Generating random variates |
| Week 5 | Selecting input probability distributions |
| Week 6 | Validation and verification |
| Week 7 | Output data analysis |
| Week 8 | Output data analysis |
| Week 9 | Midterm exam |
| Week 10 | Output data analysis |
| Week 11 | Comparing alternative system configurations (Ranking and selection) |
| Week 12 | Comparing alternative system configurations (Ranking and selection) |
| Week 13 | Variance reduction techniques |
| Week 14 | Variance reduction techniques |
| 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 | 5 | 10 |
| Presentation | 0 | 0 |
| Project | 2 | 20 |
| Seminar | 0 | 0 |
| Midterms | 1 | 20 |
| Final exam | 1 | 50 |
| Total | 100 | |
| Percentage of semester activities contributing grade succes | 8 | 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 | 0 | 0 | 0 |
| Field activities | 0 | 0 | 0 |
| Study Hours Out of Class (Preliminary work, reinforcement, ect) | 13 | 6 | 78 |
| Presentation / Seminar Preparation | 0 | 0 | 0 |
| Project | 2 | 30 | 60 |
| Homework assignment | 5 | 12 | 60 |
| Midterms (Study duration) | 1 | 25 | 25 |
| Final Exam (Study duration) | 1 | 35 | 35 |
| Total Workload | 36 | 111 | 300 |
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