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

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

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.

Citation

Naybour, S., Andrews, J., & Chiachio-Ruano, M. (2019, September). Efficient risk based optimization of large system models using a reduced petri net methodology. Presented at 29th European Safety and Reliability Conference (ESREL 2019), Hannover, Germany

• Presentation Conference Type: Edited Proceedings
• Conference Name: 29th European Safety and Reliability Conference (ESREL 2019)
• Start Date: Sep 22, 2019
• End Date: Sep 26, 2019
• Acceptance Date: Apr 11, 2019
• Publication Date: Sep 25, 2019
• Deposit Date: Jun 21, 2019
• Publicly Available Date: Jun 21, 2019
• Pages: 826-834
• Book Title: Proceedings of the 29th European Safety and Reliability Conference (ESREL 2019)
• 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
• Public URL: https://nottingham-repository.worktribe.com/output/2216011
• Publisher URL: http://itekcmsonline.com/rps2prod/esrel2019/e-proceedings/html/0212.xml
• Related Public URLs: https://esrel2019.org/#/
• Contract Date: Jun 21, 2019