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Course Description Course Name : Stochastic Models Course Code : ENM320 Course Type : Compulsory Level of Course : First Cycle Year of Study : 3 Course Semester : Spring (16 Weeks) ECTS : 5 Name of Lecturer(s) : Instructor MELİK KOYUNCU Learning Outcomes of the Course : Have knowledge about basic statistical concepts Have knowledge about Markov processes Have knowledge about queuing models Analyze simple manufacturing and service systems by queuing models Mode of Delivery : Face-to-Face Prerequisites and Co-Prerequisites : None Recommended Optional Programme Components : None Aim(s) of Course : To develop the operations research knowledge and skills by using stochastic model techniques Course Contents : Basic statistical concepts, Introduction to queuing systems, M/M/1,M/M/s and the other queue models,queuing networks, Markov chains and its applications Language of Instruction : Turkish Work Place : Classroom   Course Outline /Schedule (Weekly) Planned Learning Activities Week Subject Student's Preliminary Work Learning Activities and Teaching Methods 1 The role of probability and statistics at the Stochastic models Reading the chapter from text book Standart course tools ( board, lecture notes and teaching) 2 random variables, mean , variance Reading the chapter from text book Standart course tools ( board, lecture notes and teaching) 3 Discrete probability distributions (Bernoulli,Geometric ,Poisson etc.) Reading the chapter from text book Standart course tools ( board, lecture notes and teaching) 4 Continous probability distributions (Normal, Lognormal, Weibull, Beta etc.) Reading the chapter from text book Standart course tools ( board, lecture notes and teaching) 5 Introduction to Markov Chains Reading the chapter from text book Standart course tools ( board, lecture notes and teaching) 6 n-step transition probabilities Reading the chapter from text book Standart course tools ( board, lecture notes and teaching) 7 Midterrm exam Midterm exam 8 Steady state probabilities and first passage times Reading the chapter from text book Standart course tools ( board, lecture notes and teaching) 9 Absorbing states Reading the chapter from text book Standart course tools ( board, lecture notes and teaching) 10 Introdustion to queuing theory Reading the chapter from text book Standart course tools ( board, lecture notes and teaching) 11 Modelling the interarrival and service times Reading the chapter from text book Standart course tools ( board, lecture notes and teaching) 12 M/M/1 , M/M/s queuing models Reading the chapter from text book Standart course tools ( board, lecture notes and teaching) 13 The queues havingFinite calling population Reading the chapter from text book Standart course tools ( board, lecture notes and teaching) 14 M/G/s models and the other queing models Reading the chapter from text book Standart course tools ( board, lecture notes and teaching) 15 Jackson queuing networks and Application of queuing models at the modern manufacturing systems Reading the chapter from text book Standart course tools ( board, lecture notes and teaching) 16/17 Final exam Classical exam   Required Course Resources Resource Type Resource Name Recommended Course Material(s)     Required Course Material(s)   Assessment Methods and Assessment Criteria Semester/Year Assessments Number Contribution Percentage     Mid-term Exams (Written, Oral, etc.) 1 90     Homeworks/Projects/Others 1 10 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. 5 6 Can access information and to search/use databases and other sources for information gathering. 5 7 Works efficiently and takes responsibility both individually and as a member of a multi-disciplinary team. 5 8 Appreciates life time learning; follows scientific and technological developments and renews himself/herself continuously. 5 9 Can use computer software in industrial engineering along with information and communication technologies. 4 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. 5 13 Has a necessary consciousness on issues related to job safety and health, legal aspects of environment and engineering practice. 0 14 Becomes competent on matters related to project management, entrepreneurship, innovation and has knowledge about current matters in industrial engineering. 3 * 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 5 70 Assesment Related Works     Homeworks, Projects, Others 1 10 10     Mid-term Exams (Written, Oral, etc.) 1 2 2     Final Exam 1 2 2 Total Workload: 126 Total Workload / 25 (h): 5.04 ECTS Credit: 5