Abstract: We focus on estimating the throughput of a workflow modelled with stochastic Petri nets (SPNs). We consider this discussion important, as there is a lot of confusion about the definition of the risk and the reliability of flexible manufacturing system analysis, both being risk analysts and decision makers. We propose an approach for this analysis by using a new model for artificial social systems (ASSs) behaviours, and by introducing equivalent transfer functions for SPNs.
ASSs exist in practically every multi-agent system, and play a major role in the performance and effectiveness chart of the agents. ASS allows agents to coexist in a shared environment and pursue their respective goals in the presence of other agents.
This is the reason why we introduce a suggestive model for ASSs. To model complex systems, such as flexible manufacturing ones, a class of Petri nets is adopted, and briefly introduced. This class allows representing the flow of physical resources and control information data of the ASSs components. In the analysis of SPN we use simulations in respect to timing parameters in a generalized semi-Markov process (GSMP). By using existing results on perturbation analysis (e.g., delays in supply with raw materials, equipment failure, etc.), and by extending them to new physical interpretations we address unbiased sensitivity estimators correlated with practical solutions in order to attenuate the perturbations.
The novelty of the approach is that the construction of large Markov chains is not required. Using a structural decomposition, the construction system is divided into cells. We can simplify the structure of the SPN using the presented approach, which is useful when we deal with complex Petri nets, and we need to simplify these structures (e.g. graphs) in order to analyze them properly. For each cell a Markov model was derived and the probability was determined of at least Ni working machines in cell i, for i = 1, 2, .., n and j, where j = 1, ..., m, working material handling system (MHS) at time t, where Ni and j satisfy the system production capacity requirements. An example illustrates this approach. The results reported here form the basis of several enhancements, such as conducting performance studies of complex systems with multiple part types.

Brief Biography of the Speaker:
• Honorary Member of the Romanian Society of Electrical & Control Engineering - Member of the Romanian Technical Experts Corp.
• Technical Expert of the Romanian Ministry of Justice.
• President of the Romanian Society of Electrical & Control Engineering, Suceava Branch.
• Academic Positions: Assoc. Professor, Dept. of Automatics and Computers, Faculty of Electrical Engineering and Computer Science, “Stefan cel Mare” University of Suceava, Romania.
• Fields of Scientific Activities: Discrete Event Systems, Complex Measurement Systems, Reliability and Diagnosis of Control Systems, Environmental Management.
• He published 6 books and over 120 scientific papers in conference proceedings and journals.