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Information
| Unit | FACULTY OF SCIENCE AND LETTERS |
| COMPUTER SCIENCES PR. | |
| Code | BBZ408 |
| Name | Simulation and Modelling |
| Term | 2026-2027 Academic Year |
| Semester | 8. Semester |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 5 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | Türkçe |
| Level | Belirsiz |
| Type | Normal |
| Label | E Elective |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Doç. Dr. MURAT GENÇ |
| Course Instructor |
The current term course schedule has not been prepared yet.
|
Course Goal / Objective
The aim of this course is to provide students with the concepts, components and methods of simulation and modeling.
Course Content
This course covers the concepts and methods related to simulation and modeling components, simulation types and modeling process.
Course Precondition
None
Resources
Law, A. M. (1991). Simulation Modeling and Analysis. McGraw-Hill. Banks, J., Carson, J. S., Nelson, B. L., & Nicol, D. M. (2005). Discrete-Event System Simulation. Pearson.
Notes
Ross, S. M. (2012). Simulation. Academic Press.
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Compares simulation types with each other. |
| LO02 | Lists the components of discrete-event simulation and identifies them on a system. |
| LO03 | Creates a manual simulation table for single and multi-server queue systems and calculates performance metrics. |
| LO04 | Designs a random number generator using the linear congruential method and analyzes the generated numbers using frequency, run and autocorrelation tests. |
| LO05 | Generates random variates from different distributions using inverse transform, acceptance-rejection and Box-Muller methods. |
| LO06 | Applies histogram, QQ-plot and goodness-of-fit tests to a real dataset and determines the most appropriate distribution. |
| LO07 | Calculates confidence intervals for simulation outputs, determines warm-up period and evaluates autocorrelation. |
| LO08 | Develops a simulation project verifies the model and compares outputs of different scenarios. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Gain comprehensive knowledge of fundamental concepts, algorithms, and data structures in Computer Science. | 3 |
| PLO02 | Bilgi - Kuramsal, Olgusal | Learn essential computer topics such as software development, programming languages, and database management | 3 |
| PLO03 | Bilgi - Kuramsal, Olgusal | Understand advanced computer fields like data science, artificial intelligence, and machine learning. | |
| PLO04 | Bilgi - Kuramsal, Olgusal | Acquire knowledge of topics like computer networks, cybersecurity, and database design. | |
| PLO05 | Beceriler - Bilişsel, Uygulamalı | Develop skills in designing, implementing, and analyzing algorithms | 4 |
| PLO06 | Beceriler - Bilişsel, Uygulamalı | Gain proficiency in using various programming languages effectively | 2 |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Learn skills in data analysis, database management, and processing large datasets. | 3 |
| PLO08 | Beceriler - Bilişsel, Uygulamalı | Acquire practical experience through working on software development projects. | 4 |
| PLO09 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Strengthen teamwork and communication skills. | 2 |
| PLO10 | Yetkinlikler - Alana Özgü Yetkinlik | Foster a mindset open to technological innovations. | |
| PLO11 | Yetkinlikler - Öğrenme Yetkinliği | Encourage the capacity for continuous learning and self-improvement. | |
| PLO12 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Enhance the ability to solve complex problems | 4 |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introduction to Simulation and Basic Concepts | Reading the first chapter of the textbook | Öğretim Yöntemleri: Tartışma, Beyin Fırtınası |
| 2 | Stages of a Simulation Study | Reviewing previous week's notes | Öğretim Yöntemleri: Anlatım, Tartışma |
| 3 | Discrete Event Simulation (DES) Basics | Reading the relevant article | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 4 | Manual Simulation and Queueing Systems | Reviewing Exponential and Poisson distributions | Öğretim Yöntemleri: Problem Çözme, Anlatım, Soru-Cevap |
| 5 | Probability and Basic Distributions | Reviewing probability course notes | Öğretim Yöntemleri: Anlatım, Tartışma |
| 6 | Random Number Generators | Researching LCG algorithm | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma, Deney / Laboratuvar |
| 7 | Tests for Random Numbers | Examining test formulas | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma, Problem Çözme, Deney / Laboratuvar |
| 8 | Mid-Term Exam | Ölçme Yöntemleri: Yazılı Sınav |
|
| 9 | Random Variate Generation | Researching inverse transform formula | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 10 | Simulation from Distributions (Monte Carlo) | Reading Box-Muller article | Öğretim Yöntemleri: Anlatım, Tartışma, Alıştırma ve Uygulama, Deney / Laboratuvar |
| 11 | Monte Carlo Experiment Design | Reviewing sample size concept | Öğretim Yöntemleri: Anlatım, Problem Çözme |
| 12 | Input Data Analysis | Examining the given dataset | Öğretim Yöntemleri: Tartışma, Örnek Olay, Deney / Laboratuvar, Problem Çözme |
| 13 | Output Analysis | Reviewing confidence interval formulas | Öğretim Yöntemleri: Tartışma, Problem Çözme, Deney / Laboratuvar |
| 14 | Model Verification and System Comparison | Reviewing statistical tests | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Problem Çözme, Deney / Laboratuvar |
| 15 | Project Presentations | Submitting project report and presentation file | Öğretim Yöntemleri: Tartışma, Soru-Cevap, Bireysel Çalışma, Proje Temelli Öğrenme |
| 16 | Term Exams | Ölçme Yöntemleri: Yazılı Sınav |
|
| 17 | Term Exams | Ölçme Yöntemleri: Yazılı Sınav |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 3 | 42 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 3 | 42 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 2 | 10 | 20 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 7 | 7 |
| Final Exam | 1 | 12 | 12 |
| Total Workload (Hour) | 123 | ||
| Total Workload / 25 (h) | 4,92 | ||
| ECTS | 5 ECTS | ||