Automated time scale decomposition and analysis of stochastic Petri nets

Abstract

The automated application of time-scale decomposition to stochastic Petri nets is studied. Time-scale decomposition exploits the tendency of a system to approach a short-term equilibrium between relatively rare events and has been extensively studied in the context of Markov chains and queuing networks. Previous approaches for applying time-scale decomposition to SPN models relied heavily upon human insight in ways what hampered algorithmic implementation. A simple and effective method for specifying the time-scale decomposition of a SPN is presented, and solution techniques that take advantage of structural information from the SPN are described.<>