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Course Description Course Name : Advanced Simulation Modeling Course Code : EM-500 Course Type : Optional Level of Course : Second Cycle Year of Study : 1 Course Semester : Spring (16 Weeks) ECTS : 6 Name of Lecturer(s) : Prof.Dr. RIZVAN EROL Learning Outcomes of the Course : Understands the role of simulation among the modeling tools. Decides correctly in which problems simulation is a suitable approach. Follows all the necessary steps of simulation modeling process. Builds simulation models using at least one simulation language. Applies statistical methods used in input and output analysis of simulation modeling. Mode of Delivery : Face-to-Face Prerequisites and Co-Prerequisites : None Recommended Optional Programme Components : None Aim(s) of Course : The primary aim of this course is to study underlying probabilistic and statistical aspects of computer simulation. These aspects include modeling and estimating input processes, random-number generators, variate and process generation, statistical analysis of simulation output, ranking and selection of alternatives, variance-reduc¬tion techniques, designing simulation experiments, gradient estimation, and optimization. ARENA simulation environment will be used to develop and execute simulation programs on computer. Also, articles related to simulation practice and current state-of-the-art in simulation methodology will be reviewed. Course Contents : Basic simulation modeling process. modeling complex systems. review of current simulation software. using ARENA simulation modeling environment. model validation. selecting input probability distributions. random variate generation. experimental design issues. statistical output analysis. variance reduction techniques. optimization using simulation. Language of Instruction : English Work Place : Seminar room, computer applications lab   Course Outline /Schedule (Weekly) Planned Learning Activities Week Subject Student's Preliminary Work Learning Activities and Teaching Methods 1 Overview of simulation analysis reading the related textbook chapter lecturing, discussion 2 Modeling and estimating input processes reading the related textbook chapter lecturing, discussion 3 Random-number generation, Generation of random variates, vectors, and processes reading the related textbook chapter lecturing, discussion 4 Review of Simulation Software reading the related textbook chapter lecturing, discussion 5 Simulation Modeling Using ARENA reading the related textbook chapter lecturing, discussion 6 Simulation Modeling Using ARENA reading the related textbook chapter lecturing, discussion 7 Simulation Modeling Using ARENA reading the related textbook chapter lecturing, discussion 8 Midterm Exam preparation for the exam written exam 9 Statistical analysis of simulation output reading the related textbook chapter lecturing, discussion 10 Comparison, ranking, and selection of alternatives reading the related textbook chapter lecturing, discussion 11 Variance-reduction techniques reading the related textbook chapter lecturing, discussion 12 Designing simulation experiments reading the related textbook chapter lecturing, discussion 13 Simulation optimization reading the related textbook chapter lecturing, discussion 14 Simulation optimization reading the related textbook chapter lecturing, discussion 15 Project Presentations preparation for the presentation presentation 16/17 Final Exam preparation for the exam written exam   Required Course Resources Resource Type Resource Name Recommended Course Material(s)  KELTON, W. D., SADOWSKI, R. P. & SADOWSKI, D. A., 2009, Simulation with ARENA (2nd edition), McGraw-Hill, Inc. ISBN-13: 978-0073376288  Law, A. M. & Kelton, W. D., 2006, Simulation Modeling & Analysis (4th edition), New York, NY: Mc-Graw Hill , Inc.,ISBN-13: 978-0073294414  BANKS, J., CARSON, J. S. & NELSON, B. L., 2009, Discrete-Event System Simulation (5th 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 65     Homeworks/Projects/Others 5 35 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 Understand, interpret and apply knowledge in his/her field domain both in-depth and in-breadth by doing scientific research in industrial engineering. 3 2 Acquire comprehensive knowledge about methods and tools of industrial engineering and their limitations. 4 3 Work in multi-disciplinary teams and take a leading role and responsibility. 3 4 Identify, gather and use necessary information and data. 4 5 Complete and apply the knowledge by using scarce and limited resources in a scientific way and integrate the knowledge into various disciplines. 3 6 Keep up with the recent changes and applications in the field of Industrial Engineering and analyze these innovations when necessary. 4 7 Work in multi-disciplinary teams, take a leading role and responsibility and develop solutions for complex problems. 4 8 Analyze Industrial Engineering problems, develop innovative methods to solve the problems. 5 9 Have the ability to propose new and/or original ideas and methods in developing innovative solutions for designing systems, components or processes. 4 10 Design and perform analytical modeling and experimental research and analyze/solve complex matters emerged in this process. 5 11 Follow, study and learn new and developing applications of industrial engineering. 3 12 Use a foreign language in verbal and written communication at least B2 level of European Language Portfolio. 0 13 Present his/her research findings systematically and clearly in oral and written forms in national and international platforms. 3 14 Understand social and environmental implications of engineering practice. 3 15 Consider social, scientific and ethical values in the process of data collection, interpretation and announcement of the findings. 5 * 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 3 42     Out of Class Study (Preliminary Work, Practice) 14 3 42 Assesment Related Works     Homeworks, Projects, Others 5 7 35     Mid-term Exams (Written, Oral, etc.) 1 10 10     Final Exam 1 10 10 Total Workload: 139 Total Workload / 25 (h): 5.56 ECTS Credit: 6