Abstract
Autonomous Distributed Manufacturing Systems (ADMS) have been proposed to realise flexible control structures of manufacturing systems. In the previous researches, a real-time scheduling method based on utility values has been proposed and applied to the ADMS. Multi-agent reinforcement learning is newly proposed and implemented to the job agents and resource agents, in order to improve their coordination processes. The status, the action and the reward are defined for the individual job agents and the resource agents to evaluate the suitable utility values based on the status of the ADMS. Some case studies of the real-time scheduling have been carried out to verify the effectiveness of the proposed methods.
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Iwamura, K., Sugimura, N. (2011). Distributed Real-Time Scheduling by Using Multi-agent Reinforcement Learning. In: Wang, L., Ng, A., Deb, K. (eds) Multi-objective Evolutionary Optimisation for Product Design and Manufacturing. Springer, London. https://doi.org/10.1007/978-0-85729-652-8_11
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DOI: https://doi.org/10.1007/978-0-85729-652-8_11
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