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This paper presents a multi-head deep Q network (MHDQN) framework for solving the multi-objective dynamic flexible job shop scheduling problem (MODFJSP), focusing on minimizing total tardiness and machine idle time amidst dynamic events like job insertions. The MHDQN architecture includes a shared network layer and objective-specific layers, enabling parallel optimization of multiple objectives through an innovative action selection strategy and training mechanism. Extensive experiments demonstrate that MHD

Author

javierg.1212

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EN