Efficient Modeling of large Markov chains models with AltaRica 3.0

Michel Batteux (1) , Tatiana Prosvirnova (2) , Antoine Rauzy (3)

1 IRT SystemX
2 DTIS, ONERA, Université de Toulouse [Toulouse]
3 NTNU - Norwegian University of Science and Technology [Trondheim]

Markov chains are one of the modeling formalisms used in reliability engineering. Even if it is powerful, from a mathematical point of view, one of its big issue is the design of models for large scale systems. In fact, designing a Markov chain of a system with several components, each one may be in several states, is an important amount of job. There are no structural constructs to efficiently design such a model (e.g. composition, synchronization, etc.). In this publication, we present how the AltaRica 3.0 modeling language can be used to design efficiently large continuous time Markov chains. We consider an example of a system composed of combinations of series-parallel components, combining different states for components and different modes for parts of the system. We show that the design of the model is very efficient thanks to the advanced structural constructs of the AltaRica 3.0 modeling language. Finally, we use assessment tools available for AltaRica 3.0, e.g. the stochastic simulator, to evaluate the model of the system.

Mots clés: Markov chains; AltaRica 3.0; Model-Based Safety Assessment; production system

Domaines: Modélisation et simulation

Michel Batteux, Tatiana Prosvirnova, Antoine Rauzy. Efficient Modeling of large Markov chains models with AltaRica 3.0. Proceedings of the 31st European Safety and Reliability Conference (ESREL), Sep 2021, Angers, France. ⟨hal-03429225⟩