Stochastic discrete-event systems (SDES) capture the randomness in choices due to activity delays and the probabilities of decisions. This book delivers a comprehensive overview on modeling with a quantitative evaluation of SDES. It presents an abstract model class for SDES as a pivotal unifying result and details important model classes. The book also includes nontrivial examples to explain real-world applications of SDES. The behavior of many technical systems important in everyday life can be described using discrete states and state-changing events. Stochastic discrete-event systems (SDES) capture the randomness in choices and over time due to activity delays and the probabilities of decisions. The starting point for the evaluation of quantitative issues like performance and dependability is a formal description of the system of interest in a model. Armin Zimmermann delivers a coherent and comprehensive overview on modeling with and quantitative evaluation of SDES. An abstract model class for SDES is presented as a pivotal unifying result. Several important model classes, including queuing networks, Petri nets and automata, are detailed together with their formal translation into this abstract model class. Standard and recently developed algorithms for the performance evaluation, optimization and control of SDES are presented in the context of the abstract model class. The necessary software tool support is also covered. The book is completed with nontrivial examples from areas like manufacturing control, performance of communication systems, and supply-chain management, highlighting the application of the techniques presented. For researchers and graduate students this monograph summarizes the body of knowledge for modeling and evaluating SDES, while bringing it to a new abstraction level with the introduction of a new and unifying framework. In addition, the extensive reference list is an excellent starting point for further detailed reading and research.
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