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- W4285268002 abstract "Reconfigurable Manufacturing Systems (RMS) bring new possibilities toward meeting demand fluctuations while, at the same time, challenges scheduling efficiency. This paper presents a novel approach that, for the scheduling problem of RMS on multiple products, finds a dynamic control policy via a group of deep reinforcement learning agents. These teamed agents, embedded with a shared value decomposition network, aim on minimising the make-span of a constant updating order group by guiding a group of automated guided vehicles to move modules of machine, raw materials, and finished products inside the system." @default.
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- W4285268002 date "2022-01-01" @default.
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- W4285268002 title "Reconfigurable manufacturing system scheduling: a deep reinforcement learning approach" @default.
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- W4285268002 doi "https://doi.org/10.1016/j.procir.2022.05.131" @default.
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