| INDUSTRIAL 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 |
| The course opens with the approval of the Department at the beginning of each semester |
| Language of instruction: | En |
| Type of course: | Must Course |
| Course Level: | Bachelor |
| 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 |
| 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. |
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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: | 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 |
| Attendance | % 0 | |
| Laboratory | % 0 | |
| Application | % 0 | |
| Field Work | % 0 | |
| Special Course Internship (Work Placement) | % 0 | |
| Quizzes | 2 | % 10 |
| Homework Assignments | 4 | % 10 |
| Presentation | % 0 | |
| Project | 1 | % 20 |
| Seminar | % 0 | |
| Midterms | 1 | % 20 |
| Preliminary Jury | % 0 | |
| Final | 1 | % 40 |
| Paper Submission | % 0 | |
| Jury | % 0 | |
| Bütünleme | % 0 | |
| 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 | |
| Application | |||
| Special Course Internship (Work Placement) | |||
| Field Work | |||
| Study Hours Out of Class | 13 | 34 | |
| Presentations / Seminar | |||
| Project | 3 | 9 | |
| Homework Assignments | 2 | 6 | |
| Quizzes | 2 | 18 | |
| Preliminary Jury | |||
| Midterms | 1 | 10 | |
| Paper Submission | |||
| Jury | |||
| Final | 1 | 12 | |
| Total Workload | 145 | ||
| No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
| Program Outcomes | Level of Contribution | |
| 1) | Build up a body of knowledge in mathematics, science and industrial engineering subjects; use theoretical and applied information in these areas to model and solve complex engineering problems. | 3 |
| 2) | Identify, formulate, and solve complex engineering problems; select and apply proper analysis and modeling methods for this purpose. | 4 |
| 3) | Design a complex system, process, device or product under realistic constraints and conditions, in such a way as to meet the desired result; apply modern design methods for this purpose. The ability to apply modern design methods to meet this objective. | 3 |
| 4) | Devise, select, and use modern techniques and tools needed for solving complex problems in industrial engineering practice; employ information technologies effectively. | 3 |
| 5) | Design and conduct experiments, collect data, analyze and interpret results for investigating the complex problems specific to industrial engineering. | 5 |
| 6) | Cooperate efficiently in intra-disciplinary and multi-disciplinary teams; and show self-reliance when working independently. | |
| 7) | Demonstrate effective communication skills in both oral and written English and Turkish. Writing and understanding reports, preparing design and production reports, making effective presentations, giving and receiving clear and understandable instructions. | |
| 8) | Recognize the need for lifelong learning; show ability to access information, to follow developments in science and technology, and to continuously educate him/herself. | |
| 9) | Develop an awareness of professional and ethical responsibility, and behaving accordingly. Information about the standards used in engineering applications. | |
| 10) | Know business life practices such as project management, risk management, and change management; develop an awareness of entrepreneurship, innovation, and sustainable development. | |
| 11) | Know contemporary issues and the global and societal effects of modern age engineering practices on health, environment, and safety; recognize the legal consequences of engineering solutions. | |
| 12) | Develop effective and efficient managerial skills. |