Decision support in computer integrated manufacturing using fuzzy colored Petri nets with genetic algorithm

The basic elements of a modern manufacturing system include design automation, production automation, and shop-floor automation, which must then be integrated into a coherent system through information automation to form a fully automated factory. One very important issue currently being tackled is the development of knowledge based decision support systems which must be incorporated into all areas of automation. To build a robust knowledge base requires a systematic design methodology and a suitable modeling and analysis tool. Petri net has proven itself to be an excellent system modeling tool in computer software/hardware, communication, manufacturing, and scheduling. The work presented here is to attack the problem of diagnosing and managing shop floor which requires the incorporation of learning algorithm into current FCPN as a knowledge base developing tool. This paper first presents a systematic knowledge base design methodology and then the development of fuzzy colored Petri nets (FCPN) with the incorporation of genetic learning algorithm which can be used in developing knowledge based systems with fuzzy sets and logic.