Hybrid approach of genetic algorithms and learning automata for 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. The central issue is realization of both higher efficiency and flexibility to cope with environmental change, such as a sudden change of machining plan or breakdowns of the modules. Through the self-organization of a multi-layered strategic vector field corresponding to a task, the FTS can generate a quasi-optimal transfer path with learning automata. Also, the optimal planning is attempted by use of genetic algorithms, and is based on the global information on the system. We propose a hybridization method between the distributed and centralized approaches. Simulation is conducted to evaluate the basic system performance and the results show the effectiveness.