Course Detail
Course Detail
Course Description
| Course | Code | Semester | T+P (Hour) | Credit | ECTS |
|---|---|---|---|---|---|
| MODELLING and SIMULATION | IND3149030 | Fall Semester | 3+2 | 4 | 8 |
| Course Program |
| Prerequisites Courses | |
| Recommended Elective Courses |
| Language of Course | English |
| Course Level | First Cycle (Bachelor's Degree) |
| Course Type | Required |
| Course Coordinator | Assoc.Prof. Yasin GÖÇGÜN |
| Name of Lecturer(s) | Assoc.Prof. Yasin GÖÇGÜN |
| Assistant(s) | |
| Aim | To make students understand the real job applications and provide solutions to the job improvement processes by making simulation of them on a computer environment. |
| Course Content | This course contains; Introduction to Simulation,Input Analysis,Random-Number and Random-Variate Generation,Monte Carlo Simulation Examples,Dynamic Simulation Examples,Discrete-Event Simulation in Arena - Basic Operations Validation,Discrete-Event Simulation in Arena - Detailed Operations,Output Analysis - Terminating Simulations,Discrete-Event Simulation in Arena - Intermediate Modeling,Output Analysis - Steady State Simulations,Discrete-Event Simulation in Arena - Entity Transfer,Sample Applications I,Sample Applications II,Arena Process Analyzer. |
| Course Learning Outcomes | Teaching Methods | Assessment Methods |
| 1. Will be able to Make a hand simulation. | 12, 9 | A, E |
| 2. Will be able to apply Monte Carlo simulation. | 21, 9 | A, E |
| 3. Use Linear Congrential Method to generate a random number | 12, 9 | A, E |
| 4. Will be able to make a discrete-event simulation using Arena. | 21, 8 | A, F |
| 5. Will be able to analyze the outcomes of Arena. | 8, 9 | A, F |
| Teaching Methods: | 12: Problem Solving Method, 21: Simulation Technique, 8: Flipped Classroom Learning, 9: Lecture Method |
| Assessment Methods: | A: Traditional Written Exam, E: Homework, F: Project Task |
Course Outline
| Order | Subjects | Preliminary Work |
|---|---|---|
| 1 | Introduction to Simulation | |
| 2 | Input Analysis | |
| 3 | Random-Number and Random-Variate Generation | |
| 4 | Monte Carlo Simulation Examples | |
| 5 | Dynamic Simulation Examples | |
| 6 | Discrete-Event Simulation in Arena - Basic Operations Validation | |
| 7 | Discrete-Event Simulation in Arena - Detailed Operations | |
| 8 | Output Analysis - Terminating Simulations | |
| 9 | Discrete-Event Simulation in Arena - Intermediate Modeling | |
| 10 | Output Analysis - Steady State Simulations | |
| 11 | Discrete-Event Simulation in Arena - Entity Transfer | |
| 12 | Sample Applications I | |
| 13 | Sample Applications II | |
| 14 | Arena Process Analyzer |
| Resources |
| J. Banks, J.S. Carson, B.L. Nelson and D.M. Nicol, Discrete-Event System Simulation, 3rd Edition, Prentice Hall, 2001 W.D. Kelton, R.P. Sadowski and D.A. Sadowski, Simulation with Arena, 2nd Edition, McGraw-Hill, 2002 3. S.H. Ross, Simulation, 2nd Edition, Academic Press,1997 |
Course Contribution to Program Qualifications
| Course Contribution to Program Qualifications | |||||||
| No | Program Qualification | Contribution Level | |||||
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | Adequate knowledge in mathematics, science and engineering subjects pertaining to the relevant discipline; ability to use theoretical and applied knowledge in these areas in the solution of complex engineering problems. | X | |||||
| 2 | Ability to formulate, and solve complex engineering problems; ability to select and apply proper analysis and modeling methods for this purpose. | X | |||||
| 3 | Ability to design a complex system, process, device or product under realistic constraints and conditions, in such a way as to meet the desired result; ability to apply modern design methods for this purpose. | X | |||||
| 4 | Ability to select and use modern techniques and tools needed for analyzing and solving complex problems encountered in engineering practice; ability to employ information technologies effectively. | X | |||||
| 5 | Ability to design and conduct experiments, gather data, analyze and interpret results for investigating complex engineering problems or discipline specific research questions. | X | |||||
| 6 | Ability to work efficiently in intra-disciplinary and multi-disciplinary teams; ability to work individually. | X | |||||
| 7 | Ability to communicate effectively, both orally and in writing; knowledge of a minimum of one foreign language; ability to write effective reports and comprehend written reports, prepare design and production reports, make effective presentations, and give and receive clear and intelligible instructions. | X | |||||
| 8 | Awareness of the need for lifelong learning; ability to access information, to follow developments in science and technology, and to continue to educate him/herself. | X | |||||
| 9 | Knowledge on behavior according ethical principles, professional and ethical responsibility and standards used in engineering practices. | X | |||||
| 10 | Knowledge about business life practices such as project management, risk management, and change management; awareness in entrepreneurship, innovation; knowledge about sustainable development. | ||||||
| 11 | Knowledge about the global and social effects of engineering practices on health, environment, and safety, and contemporary issues of the century reflected into the field of engineering; awareness of the legal consequences of engineering solutions. | ||||||
Assessment Methods
| Contribution Level | Absolute Evaluation | |
| Rate of Midterm Exam to Success | 30 | |
| Rate of Final Exam to Success | 70 | |
| Total | 100 | |
| ECTS / Workload Table | ||||||
| Activities | Number of | Duration(Hour) | Total Workload(Hour) | |||
| Course Hours | 14 | 5 | 70 | |||
| Guided Problem Solving | 10 | 4 | 40 | |||
| Resolution of Homework Problems and Submission as a Report | 11 | 4 | 44 | |||
| Term Project | 0 | 0 | 0 | |||
| Presentation of Project / Seminar | 0 | 0 | 0 | |||
| Quiz | 8 | 2 | 16 | |||
| Midterm Exam | 2 | 20 | 40 | |||
| General Exam | 1 | 30 | 30 | |||
| Performance Task, Maintenance Plan | 0 | 0 | 0 | |||
| Total Workload(Hour) | 240 | |||||
| Dersin AKTS Kredisi = Toplam İş Yükü (Saat)/30*=(240/30) | 8 | |||||
| ECTS of the course: 30 hours of work is counted as 1 ECTS credit. | ||||||
Detail Informations of the Course
Course Description
| Course | Code | Semester | T+P (Hour) | Credit | ECTS |
|---|---|---|---|---|---|
| MODELLING and SIMULATION | IND3149030 | Fall Semester | 3+2 | 4 | 8 |
| Course Program |
| Prerequisites Courses | |
| Recommended Elective Courses |
| Language of Course | English |
| Course Level | First Cycle (Bachelor's Degree) |
| Course Type | Required |
| Course Coordinator | Assoc.Prof. Yasin GÖÇGÜN |
| Name of Lecturer(s) | Assoc.Prof. Yasin GÖÇGÜN |
| Assistant(s) | |
| Aim | To make students understand the real job applications and provide solutions to the job improvement processes by making simulation of them on a computer environment. |
| Course Content | This course contains; Introduction to Simulation,Input Analysis,Random-Number and Random-Variate Generation,Monte Carlo Simulation Examples,Dynamic Simulation Examples,Discrete-Event Simulation in Arena - Basic Operations Validation,Discrete-Event Simulation in Arena - Detailed Operations,Output Analysis - Terminating Simulations,Discrete-Event Simulation in Arena - Intermediate Modeling,Output Analysis - Steady State Simulations,Discrete-Event Simulation in Arena - Entity Transfer,Sample Applications I,Sample Applications II,Arena Process Analyzer. |
| Course Learning Outcomes | Teaching Methods | Assessment Methods |
| 1. Will be able to Make a hand simulation. | 12, 9 | A, E |
| 2. Will be able to apply Monte Carlo simulation. | 21, 9 | A, E |
| 3. Use Linear Congrential Method to generate a random number | 12, 9 | A, E |
| 4. Will be able to make a discrete-event simulation using Arena. | 21, 8 | A, F |
| 5. Will be able to analyze the outcomes of Arena. | 8, 9 | A, F |
| Teaching Methods: | 12: Problem Solving Method, 21: Simulation Technique, 8: Flipped Classroom Learning, 9: Lecture Method |
| Assessment Methods: | A: Traditional Written Exam, E: Homework, F: Project Task |
Course Outline
| Order | Subjects | Preliminary Work |
|---|---|---|
| 1 | Introduction to Simulation | |
| 2 | Input Analysis | |
| 3 | Random-Number and Random-Variate Generation | |
| 4 | Monte Carlo Simulation Examples | |
| 5 | Dynamic Simulation Examples | |
| 6 | Discrete-Event Simulation in Arena - Basic Operations Validation | |
| 7 | Discrete-Event Simulation in Arena - Detailed Operations | |
| 8 | Output Analysis - Terminating Simulations | |
| 9 | Discrete-Event Simulation in Arena - Intermediate Modeling | |
| 10 | Output Analysis - Steady State Simulations | |
| 11 | Discrete-Event Simulation in Arena - Entity Transfer | |
| 12 | Sample Applications I | |
| 13 | Sample Applications II | |
| 14 | Arena Process Analyzer |
| Resources |
| J. Banks, J.S. Carson, B.L. Nelson and D.M. Nicol, Discrete-Event System Simulation, 3rd Edition, Prentice Hall, 2001 W.D. Kelton, R.P. Sadowski and D.A. Sadowski, Simulation with Arena, 2nd Edition, McGraw-Hill, 2002 3. S.H. Ross, Simulation, 2nd Edition, Academic Press,1997 |
Course Contribution to Program Qualifications
| Course Contribution to Program Qualifications | |||||||
| No | Program Qualification | Contribution Level | |||||
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | Adequate knowledge in mathematics, science and engineering subjects pertaining to the relevant discipline; ability to use theoretical and applied knowledge in these areas in the solution of complex engineering problems. | X | |||||
| 2 | Ability to formulate, and solve complex engineering problems; ability to select and apply proper analysis and modeling methods for this purpose. | X | |||||
| 3 | Ability to design a complex system, process, device or product under realistic constraints and conditions, in such a way as to meet the desired result; ability to apply modern design methods for this purpose. | X | |||||
| 4 | Ability to select and use modern techniques and tools needed for analyzing and solving complex problems encountered in engineering practice; ability to employ information technologies effectively. | X | |||||
| 5 | Ability to design and conduct experiments, gather data, analyze and interpret results for investigating complex engineering problems or discipline specific research questions. | X | |||||
| 6 | Ability to work efficiently in intra-disciplinary and multi-disciplinary teams; ability to work individually. | X | |||||
| 7 | Ability to communicate effectively, both orally and in writing; knowledge of a minimum of one foreign language; ability to write effective reports and comprehend written reports, prepare design and production reports, make effective presentations, and give and receive clear and intelligible instructions. | X | |||||
| 8 | Awareness of the need for lifelong learning; ability to access information, to follow developments in science and technology, and to continue to educate him/herself. | X | |||||
| 9 | Knowledge on behavior according ethical principles, professional and ethical responsibility and standards used in engineering practices. | X | |||||
| 10 | Knowledge about business life practices such as project management, risk management, and change management; awareness in entrepreneurship, innovation; knowledge about sustainable development. | ||||||
| 11 | Knowledge about the global and social effects of engineering practices on health, environment, and safety, and contemporary issues of the century reflected into the field of engineering; awareness of the legal consequences of engineering solutions. | ||||||
Assessment Methods
| Contribution Level | Absolute Evaluation | |
| Rate of Midterm Exam to Success | 30 | |
| Rate of Final Exam to Success | 70 | |
| Total | 100 | |