A Compositional Analysis Method for Petri-Net Models

Compositional analysis aims to reveal the underlying structures of a large-scale system or network by analyzing its constituent components and their relationships. Today's mathematical modeling languages, such as Petri nets, are useful for describing distributed systems and complex networks. However, the flat model architecture of Petri nets makes it difficult for them to depict the compositional structures of a large-scale model. Therefore, an enhanced compositionality feature has become a significant demand in large-scale modeling with Petri nets. This paper explores the underlying compositional structures of a given Petri net model by using a proposed sorting algorithm. The algorithm analyses compositional structures by sorting an incidence matrix that is generated from the Petri net model. Finally, the proposed sorting algorithm is applied to a traffic network model that was built with Petri nets to analyze its compositional structures, which represent different traffic lines, with the aim of optimizing the traffic network.