A DRL-Based Reactive Scheduling Policy for Flexible Job Shops With Random Job Arrivals
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DOI: 10.1109/tase.2023.3271666
Abstract: In real-life production systems, arrivals of jobs are usually unpredictable, which makes it necessary to develop solid reactive scheduling policies to meet delivery requirements. Deep reinforcement learning (DRL) based scheduling methods are capable of quickly… read more here.
Keywords: reactive scheduling; time; drl based; policy ... See more keywords