Hybrid Approach of Genetic Algorithm and Learning Automata on Flexible Transfer System.

Abstract

The flexible transfer system (FTS) is a self-organizing manufacturing system composed of autonomous robotic modules, which transfer a palette carrying machining parts. Where, the central issue is realization of both of higher efficiency and flexibility to cope with environmental change, such as a sudden change of machining plan or break-downs of the modules. Through the self-organization of a multi-layered strategic vector field corresponding to a task, the FTS can generate quasi-optimal transfer path with Learning Automata. Also the optimal planning is attempted by use of Genetic Algorithms, which bases on the global information on the system. In this paper, we propose a hybridization method between the distributed and centralized approaches. Simulation conducted to evaluate the basic system performance and the results show the effectiveness.