@misc{10481/74303,
year = {2022},
month = {2},
url = {http://hdl.handle.net/10481/74303},
abstract = {The accurate modeling of engineering systems and processes using Petri nets often results in complex graph
representations that are computationally intensive, limiting the potential of this modeling tool in real life
applications. This paper presents a methodology to properly define the optimal structure and properties of
a reduced Petri net that mimic the output of a reference Petri net model. The methodology is based on
Approximate Bayesian Computation to infer the plausible values of the model parameters of the reduced model
in a rigorous probabilistic way. Also, the method provides a numerical measure of the level of approximation
of the reduced model structure, thus allowing the selection of the optimal reduced structure among a set
of potential candidates. The suitability of the proposed methodology is illustrated using a simple illustrative
example and a system reliability engineering case study, showing satisfactory results. The results also show
that the method allows flexible reduction of the structure of the complex Petri net model taken as reference,
and provides numerical justification for the choice of the reduced model structure.},
organization = {European Commission	859957},
organization = {Lloyd's Register Foundation (LRF), a charitable foundation in the U.K.},
publisher = {Elsevier},
keywords = {Petri nets},
keywords = {Model similarity},
keywords = {Bayesian inference},
keywords = {Approximate Bayesian Computation},
keywords = {Maintenance models},
title = {Reduction of Petri net maintenance modeling complexity via Approximate Bayesian Computation},
doi = {10.1016/j.ress.2022.108365},
author = {Chiachío Ruano, Manuel and Saleh, Ali and Chiachío Ruano, Juan},
}