Introduction to Simulation; Review of Simulation Models; Statistical Models for Simulation; Queueing Models; Inventory Systems; Random Numbers; Input Data Analysis; Output Analysis; Verification & Validation of Simulation Models; Evaluation of Alternative System Designs; Simulation of Manufacturing Systems.
Prerequisite(s)
IE4102 Statistics for Engineers
Corequisite(s)
NONE
Special Requisite(s)
The minimum qualifications that are expected from the students who want to attend the course.(Examples: Foreign language level, attendance, known theoretical pre-qualifications, etc.)
Instructor(s)
Assist. Prof. Dr. Okay Işık
Course Assistant(s)
Research Assistant Abdullah Osman
Schedule
This course is not offered in this semester.
Office Hour(s)
This course is not offered in this semester.
Teaching Methods and Techniques
Term/Report/Project: Students will be assigned in teams of three or four to work on term projects. One proposal and one final report are mandatory.
Laboratuary Work: Lab applications are done in computer labs. Statistical analysis is performed. ARENA, a simulation package, will be covered and used for simulating systems.
Principle Sources
1. Banks, J., Carson II, J.S. and Nelson, B.L. (2009). Discrete-Event System Simulation (5th ed.). Prentice Hall. 0136062121.
2. Kelton, W.D., Sadowski, R.P., and Zupick N.B. (2015). Simulation with Arena (6th ed.). McGraw-Hill. 0072919817.
Other Sources
1. Law, A.M. and Kelton, W.D. (2000). Simulation Modeling and Analysis (3rd ed.). McGraw-Hill. 0070592926.
Course Schedules
Week
Contents
Learning Methods
1. Week
Introduction. Types of Simulation. advantages and disadvantages, Monte Carlo simulation examples.
Oral presentation, Practice
2. Week
Components of discrete event simulation. Collection of statistics. Hand simulation.
Oral presentation, Practice
3. Week
Hand simulation and statistical considerations
Oral presentation, Practice
4. Week
Probability review.
Oral presentation, Practice
5. Week
Random Number Generators Generators Used by Simulation Languages. Tests for Random Numbers. Frequency for tests. Tests for autocorrelation.
Oral presentation, Practice
6. Week
Generating Random Variates. Inverse-Transform Technique. Input Distribution Fitting: Histogram, PP, and QQ chart. Input Distribution Fitting: Goodness of fit tests: Chi-square test, KS test.
Oral presentation, Practice
7. Week
Verification and Validation of Simualtion Models, Output Analysis: Comparison and Evaluation of Alternative System Designs.
Oral presentation, Practice
8. Week
MIDTERM
9. Week
Introduction to ARENA modelling framework,
Oral presentation, Practice
10. Week
Modeling basic operations with Basic Process Panel
Oral presentation, Practice
11. Week
ARENA: Advance Process Panel
Oral presentation, Practice
12. Week
ARENA: Advance Transfer Panel
Oral presentation, Practice
13. Week
Process Analyzer and OptQuest
Oral presentation, Practice
14. Week
Project presentation
Project presentation
15. Week
FINAL
16. Week
FINAL
17. Week
FINAL
Assessments
Evaluation tools
Quantity
Weight(%)
Midterm(s)
1
25
Quizzes
3
15
Project(s)
1
20
Final Exam
1
40
Program Outcomes
PO-1
Ability to apply theoretical and practical knowledge gained by Mathematics, Science and their engineering fields and ability to use their knowledge in solving complex engineering problems.
PO-2
Ability of determining, defining, formulating and solving complex engineering problems; for that purpose develop the ability of selecting and implementing suitable models and methods of analysis.
PO-3
Ability of designing a complex system, process, device or product under real world constraints and conditions serving certain needs; for this purpose ability of applying modern design techniques
PO-4
Ability of selecting and using the modern techniques and devices which are necessary for analyzing and solving complex problems in engineering implementations; ability of efficient usage of information technologies.
PO-5
Ability of designing experiments, conducting tests, collecting data and analyzing and interpreting the solutions to investigate of complex engineering problems or discipline-specific research topics.
PO-6
Ability of working efficiently in intra-disciplinary and multi-disciplinary teams; individual working ability and habits.
PO-7
Ability of verbal and written communication skills; and at least one foreign language skills, ability to write effective reports and understand written reports, ability to prepare design and production reports, ability to make impressive presentation, ability to give and receive clear and understandable instructions
PO-8
Awareness of importance of lifelong learning; ability to access data, to follow up the recent innovation in science and technology for continuous self-improvement.
PO-9
Conformity to ethical principles; knowledge about occupational and ethical responsibility, and standards used in engineering applications.
PO-10
Knowledge about work life implementations such as project management, risk management and change management; awareness about entrepreneurship and innovativeness; knowledge about sustainable development.
PO-11
Knowledge about effects of engineering applications on health, environment and security in global and social dimensions, and on the problems of the modern age in engineering; awareness about legal outcomes of engineering solutions.
Learning Outcomes
LO-1
Gains knowledge about the basic concepts used in simulation modeling.
LO-2
Gains the ability to create, verify and validate a discrete event simulation model using modern tools.
LO-3
Gains the ability to analyze and compare simulation outputs using statistical tools.
LO-4
Gains the ability to analyze a system to determine its needs and constraints.
LO-5
Gains the ability to conduct simulation experiments and report results according to established procedures.
LO-6
Gains the ability to function effectively as a member of a team, and the ability to present the results of the teamwork in written, verbal and graphical forms.
