DECISION LEARNING ABOUT PRODUCTION CONTROL AS MACHINES BREAK DOWN IN A FLEXIBLE MANUFACTURING SYSTEM

During manufacturing, there are many situations that can affect produc tion performance. Such situations include machine breakdowns, rush ord ers, order changes, and order delays. When such issues occur, one has to make decisions to try to maintain production efficiency. Human deci sions tend to be too late and incomplete in such contingencies. Thus a system that can make better decisions in time to maintain production performance is needed. To achieve this objective, the intelligent deci sion system described in this paper integrates artificial intelligence , an optimization technique, and simulation to serve such problems. Th e decision-making logic of the intelligent decision system is describe d by event graphs. It imitates the manner of human thinking. Self-lear ning of the decision-making process is used to strengthen the decision quality. In this study, a method of rule induction is applied to buil d up the self-learning system. There are two subsystems included in th is system. One is rule generation and the other is knowledge managemen t. A case for machine breakdowns is presented and discussed. A series of tests designed to validate the self-learning system are presented. These demonstrate that a rule induction method is suitable for constru cting the self-learning.