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Efficient risk based optimization of large system models using a reduced petri net methodology

Naybour, Susannah; Andrews, John; Chiachio-Ruano, Manuel

Authors

Susannah Naybour

John Andrews

Manuel Chiachio-Ruano



Contributors

Michael Beer
Editor

Enrico Zio
Editor

Abstract

The methodology presented in this paper is a two-stage optimization approach that can be applied to large system level models, in this case using a Stochastic Petri Net (SPN) framework, to produce an equivalent model response at a reduced computational cost. The method consists of generating a reduced SPN which approximates the behaviour of its large counterpart with a shorter simulation time. Parameters in this reduced structure are updated following a combined Approximate Bayesian Computation and Subset Simulation framework. In the first stage, optimization of the reduced model via a Genetic Algorithm provides a first approximation of the optimal solutions for the full system level model. In the second stage, these approximate optimal solutions then form the starting point of a short optimization of the large SPN to fine tune the results using a reduced solution space. This method is demonstrated for a sub-section of an SPN of a fire protection system. Optimization of the full model with a Genetic Algorithm is compared to the optimization through this two-stage approach to demonstrate the capability of the methodology. Results show good model agreement at a reduced computational cost.

Start Date Sep 22, 2019
Publication Date Sep 25, 2019
Book Title Proceedings of the 29th European Safety and Reliability Conference (ESREL 2019)
Institution Citation Naybour, S., Andrews, J., & Chiachio-Ruano, M. (2019). Efficient risk based optimization of large system models using a reduced petri net methodology. In M. Beer, & E. Zio (Eds.), Proceedings of the 29th European Safety and Reliability Conference (ESREL 2019)
Keywords Petri nets, Risk, optimization, Genetic algorithms, Approximate Bayesian computation, Subset simulation
Publisher URL http://itekcmsonline.com/rps2prod/esrel2019/e-proceedings/html/0212.xml
Related Public URLs https://esrel2019.org/#/

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