A reactive self-organised scheduling based on multi-agent paradigm

This paper denotes the abilities of multi-agent paradigm with reinforcement learning algorithm to obtain a sophisticated work movement in job shop type manufacturing scheduling problem. Several scheduling objectives are conducted by the social goal of the agents. Learning algorithm is introduced to improve the agent movements without losing their robustness. The well-educated work agents prove capable of robustness and reliability coping with major scheduling demands through several simulation scenarios.