Backward Coupling in Bounded Free-Choice Nets Under Markovian and Non-Markovian Assumptions

  • Anne Bouillard
  • Bruno Gaujal


In this paper, we show how to design a perfect sampling algorithm for stochastic Free-Choice Petri nets by backward coupling. For Markovian event graphs, the simulation time can be greatly reduced by using extremal initial states, namely blocking marking, although such nets do not exhibit any natural monotonicity property. Another approach for perfect simulation of non-Markovian event graphs is based on a (max,plus) representation of the system and the theory of (max,plus) stochastic systems. We also show how to extend this approach to one-bounded free choice nets to the expense of keeping all states. Finally, experimental runs show that the (max,plus) approach needs a larger simulation time than the Markovian approach.


Petri nets Perfect simulation Heaps of pieces Max-plus systems 



The authors would like to thank Jean Mairesse for his precious advices.


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Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  1. 1.IRISA/ENS Cachan (Bretagne)BruzFrance
  2. 2.INRIA and Lab. LIG (CNRS, INPG, UJF)MontbonnotFrance

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