Application of Generalized Stochastic Petri Nets for Performance Analysis of a Production Process

The increasing complexity of modern manufacturing systems, combined with the continuous need of performance improvements, requires tools to assess the metrics and the quality of possible solutions. This paper focuses on the application of the Generalized Stochastic Petri Net (GSPN) for performance analysis of a production process. To solve the problem of increasing computational burden for large-scale GSPNs, the proposed approach exploits a partition of the overall plant model in several submodules. The challenge is to integrate the results of the analysis of all of the submodules, considering the effect of each one on the previous and subsequent submodules, thus achieving information on the performance of the whole GSPN. The method is applied to evaluate the performance of an automotive lead acid battery production chain, demonstrating its effectiveness in managing complexity and enhancing computational efficiency.