Abstract
Multi-agent reinforcement learning have been applied to the autonomous distributed scheduling method in order to improve the objective functions of individual job agents and resource agents, in the previous researches. New distributed scheduling method is proposed to improve the sum of earliness and tardiness of all job agents by applying multi-agent reinforcement learning to resource agents, in this research. The resource agents learn the selection criteria of job agents for next machining operation based on the status of manufacturing systems. Some case studies have been carried out to verify the effectiveness of the proposed method.
