Abstract

Petri nets provide a compact and graphical way to model large and complex discrete event systems (DES). For such systems, the state-space explosion is problematic. Fluid stochastic event graphs are decision free Petri nets, which can represent systems with failures. This paper presents an estimation algorithm for state space estimation and optimization of failure-prone DES.

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