Intelligent manufacturing system by the evolutionary computation-scheduling and planning of the flexible transportation

This paper deals with scheduling and planning in an intelligent manufacturing system. We have proposed self-organizing manufacturing systems which are composed of a number of autonomous modules. Each module decides output through the interaction with other modules. In this paper, we focus on a self-organizing conveyer system and apply an evolutionary computation method to path planning problems on its flexible transportation. There are two approaches to solve the path planning problems. One is global preplanning under the known work space. This global preplanning can obtain the optimal path, but cannot treat dynamic work space. The other is local decision making under the dynamic work space. Therefore, we must take into account the global optimization and local adaptability to reduce manufacturing cost. Consequently, we divide the path planning problem into some subproblems. The subproblems are locally solved by a genetic algorithm. Furthermore, we discuss the effectiveness of the proposed method through computer simulation.