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AltaRica 3.0 assertions: The whys and wherefores

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  • Michel Batteux
  • Tatiana Prosvirnova
  • Antoine Rauzy

Abstract

In discrete event simulations, the system is assumed to change of state when and only when an event occurs. This change of state can be more or less sophisticated depending on the modeling formalism. In this article, we discuss the whys and wherefores of the fixpoint assertion mechanism introduced in AltaRica 3.0 to perform changes of states. We show how it can be used to handle complex phenomena such as change in flow directions depending on the states of components. We propose an efficient implementation of this mechanism, thanks to ideas stemmed in theoretical computer science and artificial intelligence. We compare the AltaRica 3.0 approach with alternative ones, including those of the previous versions of the language.

Suggested Citation

  • Michel Batteux & Tatiana Prosvirnova & Antoine Rauzy, 2017. "AltaRica 3.0 assertions: The whys and wherefores," Journal of Risk and Reliability, , vol. 231(6), pages 691-700, December.
    • Handle: RePEc:sae:risrel:v:231:y:2017:i:6:p:691-700
    DOI: 10.1177/1748006X17728209
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    References listed on IDEAS

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    1. Enrico Zio, 2013. "System Reliability and Risk Analysis," Springer Series in Reliability Engineering, in: The Monte Carlo Simulation Method for System Reliability and Risk Analysis, edition 127, chapter 0, pages 7-17, Springer.
    2. Enrico Zio, 2013. "Monte Carlo Simulation: The Method," Springer Series in Reliability Engineering, in: The Monte Carlo Simulation Method for System Reliability and Risk Analysis, edition 127, chapter 0, pages 19-58, Springer.
    3. Boiteau, M. & Dutuit, Y. & Rauzy, A. & Signoret, J.-P., 2006. "The AltaRica data-flow language in use: modeling of production availability of a multi-state system," Reliability Engineering and System Safety, Elsevier, vol. 91(7), pages 747-755.
    4. Enrico Zio, 2013. "The Monte Carlo Simulation Method for System Reliability and Risk Analysis," Springer Series in Reliability Engineering, Springer, edition 127, number 978-1-4471-4588-2, January.
    5. Enrico Zio, 2013. "System Reliability and Risk Analysis by Monte Carlo Simulation," Springer Series in Reliability Engineering, in: The Monte Carlo Simulation Method for System Reliability and Risk Analysis, edition 127, chapter 0, pages 59-81, Springer.
    6. Brameret, P.-A. & Rauzy, A. & Roussel, J.-M., 2015. "Automated generation of partial Markov chain from high level descriptions," Reliability Engineering and System Safety, Elsevier, vol. 139(C), pages 179-187.
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    Cited by:

    1. Meng, Huixing & Kloul, Leïla & Rauzy, Antoine, 2018. "Modeling patterns for reliability assessment of safety instrumented systems," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 111-123.

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