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Course Description Course Name : Simulation Modeling Course Code : ENM332 Course Type : Compulsory Level of Course : First Cycle Year of Study : 3 Course Semester : Spring (16 Weeks) ECTS : 5 Name of Lecturer(s) : Instructor CENK ŞAHİN Learning Outcomes of the Course : Learning basics of simulation Learning statistical analysis methods using in simulation Learning data collection and techniques for distribution fitting Learning creating simulation models in computer environment Manage to do validation tests of simulation models Output analysis and compare alternative sytems Learning using simulation package programs Random numbers and learn the techniques of generating random numbers Mode of Delivery : Face-to-Face Prerequisites and Co-Prerequisites : None Recommended Optional Programme Components : None Aim(s) of Course : 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. Course Contents : 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 Language of Instruction : Turkish Work Place : Classroom, Laboratory   Course Outline /Schedule (Weekly) Planned Learning Activities Week Subject Student's Preliminary Work Learning Activities and Teaching Methods 1 Basic Simulation Concepts Reading lecture notes and references about the subject Lecture, laboratory 2 Discrete Event Simulation and Modelling Structures Reading lecture notes and references about the subject Lecture, laboratory 3 Selection of Probability Distributions-I Reading lecture notes and references about the subject Lecture, laboratory 4 Selection of Probability Distributions-II Reading lecture notes and references about the subject Lecture, laboratory 5 Hypothesis Tests Reading lecture notes and references about the subject Lecture, laboratory 6 Random Number Generators and Generating Random Numbers from Distributions Reading lecture notes and references about the subject Lecture, laboratory 7 Analyzing Simulation Softwares: SIMAN-Simulation Language and ARENA-Model Development Environment Reading lecture notes and references about the subject Lecture, laboratory 8 Midterm Exam Study for exam Exam 9 ARENA Examples and Applications-I Reading lecture notes and references about the subject Lecture, laboratory 10 ARENA Examples and Applications-II Reading lecture notes and references about the subject Lecture, laboratory 11 ARENA Examples and Applications-III Reading lecture notes and references about the subject Lecture, laboratory 12 ARENA Examples and Applications-IV Reading lecture notes and references about the subject Lecture, laboratory 13 Validation Tests of Simulation Models Reading lecture notes and references about the subject Lecture, laboratory 14 Output Analysis and Comparing Alternative Systems´ Design Reading lecture notes and references about the subject Lecture, laboratory 15 Variance Reduction Techniques Reading lecture notes and references about the subject Lecture, laboratory 16/17 Final Exam Study for exam Exam   Required Course Resources Resource Type Resource Name Recommended Course Material(s)  Lecture notes by Prof.Dr Rızvan EROL  LAW, A. M. & KELTON, W. D., 2000, Simulation Modeling & Analysis, New York, NY: Mc-Graw Hill , Inc.  KELTON, W. D., SADOWSKI, R. P. & STURROCK, D. T., 2007, Simulation with ARENA, McGraw-Hill, Inc.  BANKS, J., CARSON, J. S. & NELSON, B. L., 1996, Discrete-Event System Simulation (2nd edition), New York, NY: Prentice-Hall, Inc. Required Course Material(s)   Assessment Methods and Assessment Criteria Semester/Year Assessments Number Contribution Percentage     Mid-term Exams (Written, Oral, etc.) 1 70     Homeworks/Projects/Others 2 30 Total 100 Rate of Semester/Year Assessments to Success 40   Final Assessments 100 Rate of Final Assessments to Success 60 Total 100   Contribution of the Course to Key Learning Outcomes # Key Learning Outcome Contribution* 1 Can collect and analyze data required for industrial engineering problems ,develops and evaluates alternative solutions. 5 2 Has sufficient background on topics related to mathematics, physical sciences and industrial engineering. 5 3 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. 5 4 Gains ability to analyze a service and/or manufacturing system or a process and describes, formulates and solves its problems . 5 5 Gains ability to choose and apply methods and tools for industrial engineering applications. 4 6 Can access information and to search/use databases and other sources for information gathering. 3 7 Works efficiently and takes responsibility both individually and as a member of a multi-disciplinary team. 4 8 Appreciates life time learning; follows scientific and technological developments and renews himself/herself continuously. 4 9 Can use computer software in industrial engineering along with information and communication technologies. 5 10 Can use oral and written communication efficiently. 4 11 Has a conscious understanding of professional and ethical responsibilities. 4 12 Uses English skills to follow developments in industrial engineering and to communicate with people in his/her profession. 4 13 Has a necessary consciousness on issues related to job safety and health, legal aspects of environment and engineering practice. 3 14 Becomes competent on matters related to project management, entrepreneurship, innovation and has knowledge about current matters in industrial engineering. 4 * Contribution levels are between 0 (not) and 5 (maximum).   Student Workload - ECTS Works Number Time (Hour) Total Workload (Hour) Course Related Works     Class Time (Exam weeks are excluded) 14 4 56     Out of Class Study (Preliminary Work, Practice) 14 2 28 Assesment Related Works     Homeworks, Projects, Others 2 5 10     Mid-term Exams (Written, Oral, etc.) 1 10 10     Final Exam 1 10 10 Total Workload: 114 Total Workload / 25 (h): 4.56 ECTS Credit: 5