Intelligent control of self-organizing manufacturing system with local learning mechanism

This paper deals with intelligent control of self-organizing manufacturing system (SOMS). SOMS is composed of modules which self-organize according to time series of information from other modules and environment. Each module makes outputs through the interaction with other modules. We consider a manufacturing line composed of conveyer units and machining centers. In this paper, we discuss intelligent control and path planning in the manufacturing line. A genetic algorithm is applied to path planning of pallets on conveyor units as global decision making and learning automaton is applied to local decision making of each conveyor unit. In addition, we use simplified fuzzy inference to the proving control of pallets. Finally, we discuss the effectiveness of the proposed method through computer simulations.