### Abstract

Original language | English |
---|---|

Publisher | Springer |

Number of pages | 17 |

Place of Publication | Berlin, Heidelberg |

ISBN (Print) | 978-3-642-40195-4 |

DOIs | |

Publication status | Published - 2013 |

Externally published | Yes |

### Fingerprint

### Keywords

- EWI-23929
- IR-87777
- METIS-300136

### Cite this

*Berlin, Heidelberg: Springer*. https://doi.org/10.1007/978-3-642-40196-1

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*Automated rare event simulation for stochastic Petri nets*. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40196-1

**Automated rare event simulation for stochastic Petri nets.** / Reijsbergen, D.P.; de Boer, Pieter-Tjerk; Scheinhardt, Willem R.W.; Haverkort, Boudewijn R.H.M.; Joshi, K. (Editor); Siegle, M.; Stoelinga, M.; d' Argenio, P.R. (Editor).

Research output: Other contribution › Other research output

TY - GEN

T1 - Automated rare event simulation for stochastic Petri nets

AU - Reijsbergen, D.P.

AU - de Boer, Pieter-Tjerk

AU - Scheinhardt, Willem R.W.

AU - Haverkort, Boudewijn R.H.M.

AU - Siegle, M.

AU - Stoelinga, M.

A2 - Joshi, K.

A2 - d' Argenio, P.R.

N1 - 10.1007/978-3-642-40196-1

PY - 2013

Y1 - 2013

N2 - We introduce an automated approach for applying rare event simulation to stochastic Petri net (SPN) models of highly reliable systems. Rare event simulation can be much faster than standard simulation because it is able to exploit information about the typical behaviour of the system. Previously, such information came from heuristics, human insight, or analysis on the full state space. We present a formal algorithm that obtains the required information from the high-level SPN- description, without generating the full state space. Essentially, our algorithm reduces the state space of the model into a (much smaller) graph in which each node represents a set of states for which the most likely path to failure has the same form. We empirically demonstrate the efficiency of the method with two case studies.

AB - We introduce an automated approach for applying rare event simulation to stochastic Petri net (SPN) models of highly reliable systems. Rare event simulation can be much faster than standard simulation because it is able to exploit information about the typical behaviour of the system. Previously, such information came from heuristics, human insight, or analysis on the full state space. We present a formal algorithm that obtains the required information from the high-level SPN- description, without generating the full state space. Essentially, our algorithm reduces the state space of the model into a (much smaller) graph in which each node represents a set of states for which the most likely path to failure has the same form. We empirically demonstrate the efficiency of the method with two case studies.

KW - EWI-23929

KW - IR-87777

KW - METIS-300136

U2 - 10.1007/978-3-642-40196-1

DO - 10.1007/978-3-642-40196-1

M3 - Other contribution

SN - 978-3-642-40195-4

PB - Springer

CY - Berlin, Heidelberg

ER -