GSPN-based modeling and analysis for robotized assembly system

Generalized stochastic Petri-nets (GSPN) is a kind of high-level abstraction modeling tool for a system with complex state transitions. A GSPN model will conduce to rational decision making during a design process for a system. In this paper a robotized assembly system with complex workflow (assembly tasks flow) is studied, and GSPN model is used to analyze deeply qualitative and quantitative properties of the robotized assembly system. The model will facilitate assessing average performance and testing correctness and consistent of operating sequences of the robotized assembly system. Several performance indexes of GSPN model are formulated such as the probability of a state, math expectation of tokens of a place, firing rate of exponent transitions and average waiting time of a place. On the basis of the method, an a robotized assembly workstation for a automotive transmission component is designed and built successfully.