A hybrid approach in intelligent workflow modelling using Petri nets and neural network for inter-organizational cooperation

With business applications´ going toward collectivization, interorganization, internationalization, many researches have been launched about intelligent workflow for interorganizational cooperation. Petri nets are powerful and versatile tools for modeling, simulating, analyzing and designing of complex workflow systems. This work mainly discusses a hybrid approach using neural network and Petri nets in the formal model of intelligent workflow for interorganizational cooperation. The model is called intelligent neural extended Petri nets (INEPN). INEPN not only takes the descriptive advantages of Petri nets, but also has learning ability like neural network INEPN is suitable for dynamic process and information, i.e., the weights of INEPN are adjustable. Based on INEPN, an intelligent WfMS is developed for interorganizational cooperation in manufacturing industry. The INEPN model is an innovative method for intelligent workflow.