Performance optimization for a class of Generalized Stochastic Petri nets

This paper considers the problem of optimizing the (long-term) performance of operations that are modeled by Generalized Stochastic Petri nets. The proposed methodology employs the representational power of the GSPN framework in order to articulate an explicit trade-off between the computational tractability of the formulated problem and the operational efficiency of the derived solutions. On the other hand, the solution of the considered formulations is based on recent results regarding the sensitivity analysis of Markov reward processes. A more expansive treatment of the presented results, together with a case study that highlights the relevance of the considered problem and the efficacy of the proposed methodology, can be found in a companion document that is accessible from the website of the second author.