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COURSE INFORMATON
Course Title Code Semester L+P Hour Credits ECTS
Simulation Modelling ENM   455 7 3 3 4

 Prerequisites and co-requisites Recommended Optional Programme Components None

Language of Instruction Turkish
Course Level First Cycle Programmes (Bachelor's Degree)
Course Type
Course Coordinator Assoc.Prof.Dr. Cenk ŞAHİN
Instructors
 Doç.Dr. CENK ŞAHİN 1. Öğretim Grup:A

Assistants
Goals
The aim of this course is to teach the simulation modelling and analysis technique as an approach to analyse the problems of industrial engineering. In this course it will be focused on the statistical analysis methods used in simulation modelling as well as the creation of simulation models in computer environment.
Content
Basic Simulation Concepts, Discrete Event Simulation and Modelling Structure, Selection of Probability Distributions, Hypothesis Tests, Random Number Generators and Generating Random Numbers from Distributions, Analyzing Simulation Softwares SIMAN Simulation Language and ARENA Model Development Environment, ARENA Examples and Applications, Validation Tests of Simulation Models, Output Analysis and Comparing Alternative Systems' Design, Variance Reduction Techniques

Learning Outcomes
1) Learning basics of simulation
2) Learning statistical analysis methods using in simulation
3) Learning data collection and techniques for distribution fitting
4) Learning creating simulation models in computer environment
5) Manage to do validation tests of simulation models
6) Output analysis and compare alternative systems
7) Learning using simulation package programs
8) Random numbers and learning the techniques of generating random numbers
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Course's Contribution To Program
NoProgram Learning OutcomesContribution
12345
1
Has sufficient background on topics related to mathematics, physical sciences and industrial engineering.
X
2
Gains ability to use the acquired theoretical knowledge on basic sciences and industrial engineering for describing, formulating and solving an industrial engineering problem, and to choose appropriate analytical and modeling methods.
X
3
Gains ability to analyze a service and/or manufacturing system or a process and describes, formulates and solves its problems .
X
4
Gains ability to choose and apply methods and tools for industrial engineering applications.
X
5
Can collect and analyze data required for industrial engineering problems ,develops and evaluates alternative solutions.
X
6
Works efficiently and takes responsibility both individually and as a member of a multi-disciplinary team.
X
7
Can access information and to search/use databases and other sources for information gathering.
X
8
Appreciates life time learning; follows scientific and technological developments and renews himself/herself continuously.
X
9
Can use computer software in industrial engineering along with information and communication technologies.
X
10
Can use oral and written communication efficiently.
X
11
Uses English skills to follow developments in industrial engineering and to communicate with people in his/her profession.
X
12
Has a conscious understanding of professional and ethical responsibilities.
X
13
Has a necessary consciousness on issues related to job safety and health, legal aspects of environment and engineering practice.
X
14
Becomes competent on matters related to project management, entrepreneurship, innovation and has knowledge about current matters in industrial engineering.
X

Course Content
WeekTopicsStudy Materials _ocw_rs_drs_yontem
1 Basic Simulation Concepts Reading lecture notes and references about the subject Lecture
2 Discrete Event Simulation and Modelling Structures Reading lecture notes and references about the subject Lecture
Discussion
Homework
3 Selection of Probability Distributions-I Reading lecture notes and references about the subject Lecture
Discussion
Drilland Practice
4 Selection of Probability Distributions-II Reading lecture notes and references about the subject Lecture
Discussion
Drilland Practice
5 Hypothesis Tests Reading lecture notes and references about the subject Lecture
Discussion
Drilland Practice
Problem Solving
6 Random Number Generators and Generating Random Numbers from Distributions Reading lecture notes and references about the subject Lecture
Discussion
Drilland Practice
Problem Solving
Homework
7 Analyzing Simulation Softwares: SIMAN-Simulation Language and ARENA-Model Development Environment Reading lecture notes and references about the subject Lecture
Simulation
8 Midterm Exam Reading and going over lecture notes and references about the subject tough Testing
9 ARENA Examples and Applications-I Reading lecture notes and references about the subject Lecture
Drilland Practice
Lab / Workshop
Problem Solving
10 ARENA Examples and Applications-II Reading lecture notes and references about the subject Lecture
Drilland Practice
Lab / Workshop
Problem Solving
11 ARENA Examples and Applications-III Reading lecture notes and references about the subject Lecture
Drilland Practice
Lab / Workshop
Problem Solving
12 ARENA Examples and Applications-IV Reading lecture notes and references about the subject Lecture
Drilland Practice
Lab / Workshop
Problem Solving
13 Validation Tests of Simulation Models Reading lecture notes and references about the subject Lecture