@inproceedings { , title = {Efficient risk based optimization of large system models using a reduced petri net methodology}, 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.}, conference = {29th European Safety and Reliability Conference (ESREL 2019)}, doi = {10.3850/978-981-11-2724-3\_ 0212-cd}, pages = {826-834}, publicationstatus = {Published}, url = {https://nottingham-repository.worktribe.com/output/2216011}, keyword = {Petri nets, Risk, optimization, Genetic algorithms, Approximate Bayesian computation, Subset simulation}, year = {2019}, author = {Naybour, Susannah and Andrews, John and Chiachio-Ruano, Manuel} editor = {Beer, Michael and Zio, Enrico} }