SOFTWARE ENGINEERING | |||||
Bachelor | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF-LLL: Level 6 |
Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
INE4011 | System Simulation | Fall | 2 | 2 | 3 | 6 |
This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester. |
Language of instruction: | English |
Type of course: | Non-Departmental Elective |
Course Level: | Bachelor’s Degree (First Cycle) |
Mode of Delivery: | Face to face |
Course Coordinator : | Prof. Dr. MUSTAFA ÖZBAYRAK |
Course Lecturer(s): |
Prof. Dr. MUSTAFA ÖZBAYRAK RA ESRA ADIYEKE Prof. Dr. FAİK TUNÇ BOZBURA |
Recommended Optional Program Components: | None |
Course Objectives: | This course is designed for junior level Industrial Engineering and close disciplines' students to give the fundamental concepts of modelling and analysis of discrete systems. The course aims to provide rigorous input and output analyses of the simulation model created using the statistical and probabilistic concepts as well as modelling the discrete systems, with the examples from both manufacturing and service systems using a general purpose simulation software. |
The students who have succeeded in this course; I. Recognize the basic principles of simulation modeling. II. Define and use appropriate performance metrics when modeling a system. III. Recognize the basic concepts of a discrete event simulation model including model components, flowchart, and event list. IV. Collect and manage performance measurement data. V. Data collection or production from a sample data set. Statistical analysis of the sample data to estimate or approximate the probabilistic distribution and its parameters. VI. Modelling and analysis of discrete event simulation models of both manufacturing and service systems using generic simulation program called ARENA. VII. Developing simulation models that address critical research issues and/or industrial systems. VIII. Recognizing how a computer simulation program can be used to model complex systems and solve related decision problems under different working conditions, which are presented through several what-if scenarios and analyses. IX. Running a simulation model under different scenarios through either short-term but with consecutive replications or one very long run to get turely random output and their statistical analyses. X. Apply a simulation project from start to finish following the stages, data collection or generation, designing the model, building the model, creating the working scenarios, running the simulation with multiple replications and statistical analyses of the output generated. |
This course is an alternative modelling method to mathematical optimization to model the complex systems. This course aims to teach the fundamental principles of simulation modelling, its steps, data creation, design and create a simulation model with the help of a simulation software, running the model under different system scenarios, obtaining the output as well as analysing and reporting output. |
Week | Subject | Related Preparation |
1) | Introduction to Simulation modeling | |
2) | A guided tour of modelling steps in Simulation. | |
3) | Statistics and Probability for Simulation Modelling I | |
4) | Statistics and Probability for Simulation Modelling II | |
5) | Modelling a simple system using ARENA | |
6) | System Modelling I | |
7) | System Modelling II | |
8) | Selecting the Input Analysis I | |
9) | Selecting Input Probability Distribution II | |
10) | System Modelling I | |
11) | Random Number Generation and Animasions | |
12) | Modelling Complex Systems | |
13) | Output Analysis | |
14) | Output Analysis II | |
15) | Entity Transfer in Modelling | |
16) | Simulation of Manufacturing Systems |
Course Notes / Textbooks: | W. D. Kelton, R. P. Sadowski, D. T. Sturrock, Simulation with Arena-6th Edition, McGraw-Hill, 2015. J. Banks, J. S. Carson II, B. L. Nelson, D.M. Nicol, Discrete-Event System Simulation, 5th Edition, Prentice Hall, 2010. |
References: | Lecture Notes and supporting materials collected from several academic resources as well as company reports and white papers. |
Semester Requirements | Number of Activities | Level of Contribution |
Quizzes | 2 | % 10 |
Homework Assignments | 4 | % 10 |
Project | 1 | % 20 |
Midterms | 1 | % 20 |
Final | 1 | % 40 |
Total | % 100 | |
PERCENTAGE OF SEMESTER WORK | % 40 | |
PERCENTAGE OF FINAL WORK | % 60 | |
Total | % 100 |
Activities | Number of Activities | Workload |
Course Hours | 14 | 28 |
Laboratory | 14 | 28 |
Study Hours Out of Class | 13 | 34 |
Project | 3 | 9 |
Homework Assignments | 2 | 6 |
Quizzes | 2 | 18 |
Midterms | 1 | 10 |
Final | 1 | 12 |
Total Workload | 145 |
No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
Program Outcomes | Level of Contribution | |
1) | Be able to specify functional and non-functional attributes of software projects, processes and products. | |
2) | Be able to design software architecture, components, interfaces and subcomponents of a system for complex engineering problems. | |
3) | Be able to develop a complex software system with in terms of code development, verification, testing and debugging. | |
4) | Be able to verify software by testing its program behavior through expected results for a complex engineering problem. | |
5) | Be able to maintain a complex software system due to working environment changes, new user demands and software errors that occur during operation. | |
6) | Be able to monitor and control changes in the complex software system, to integrate the software with other systems, and to plan and manage new releases systematically. | |
7) | Be able to identify, evaluate, measure, manage and apply complex software system life cycle processes in software development by working within and interdisciplinary teams. | |
8) | Be able to use various tools and methods to collect software requirements, design, develop, test and maintain software under realistic constraints and conditions in complex engineering problems. | |
9) | Be able to define basic quality metrics, apply software life cycle processes, measure software quality, identify quality model characteristics, apply standards and be able to use them to analyze, design, develop, verify and test complex software system. | |
10) | Be able to gain technical information about other disciplines such as sustainable development that have common boundaries with software engineering such as mathematics, science, computer engineering, industrial engineering, systems engineering, economics, management and be able to create innovative ideas in entrepreneurship activities. | |
11) | Be able to grasp software engineering culture and concept of ethics and have the basic information of applying them in the software engineering and learn and successfully apply necessary technical skills through professional life. | |
12) | Be able to write active reports using foreign languages and Turkish, understand written reports, prepare design and production reports, make effective presentations, give clear and understandable instructions. | |
13) | Be able to have knowledge about the effects of engineering applications on health, environment and security in universal and societal dimensions and the problems of engineering in the era and the legal consequences of engineering solutions. |