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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
Pages 826-834
Book Title Proceedings of the 29th European Safety and Reliability Conference (ESREL 2019)
APA6 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), 826-834. https://doi.org/10.3850/978-981-11-2724-3_+0212-cd
DOI https://doi.org/10.3850/978-981-11-2724-3_+0212-cd
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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