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This text covers modelling and simulation of discrete-event systems (DES). Examples include computer communications networks, flexible manufacturing systems, PERT (project evaluation and review techniques) networks and flow networks including queuing systems. These systems are typically driven by the occurrence of discrete-events and their changes in time. In view of the complex interactions among such discrete events, the corresponding systems are often studied via stochastic simulation.

This study allows readers to get to grips with the conceptual tools and practical techniques for building robust machine learning in the face of adversaries.