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The goal of this course is to teach descriptive models to random events arising in manufacturing and service operations. The models covered are Poisson processes, renewal theory, and discrete and continuous time Markov chains. The course also presents the applications of stochastic processes to solve engineering related problems.
Risk definition and analysis. Types of uncertainty. Sources of uncertainty. Risk prediction, assessment and control. Quantitative risk assessment models. Making probabilistic inferences. Probabilistic scenario and sensitivity analysis. Monte-Carlo simulation. Decision making under uncertainty.
Upon succesful completion of this course, a student will be able to
1. Describe a stochastic process (B1, a1)
2. Analyze and identify a stochastic process (B4, e)
3. Generate stochastic models to describe random events in production systems (B5, c)
4. Critically assess the use of stochastic processes in solving complex contemporary problems of manufacturing and service systems (B6, h)
5. Recognize the need of and engage in life-long learning (i)