Dynamic Job Shop Scheduling via Deep Reinforcement Learning
2023 IEEE 35TH INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE, ICTAI(2023)
摘要
Recently, deep reinforcement learning (DRL) is shown to be promising in learning dispatching rules end-to-end for complex scheduling problems. However, most research is limited to deterministic problems. In this paper, we focus on the dynamic job-shop scheduling problem (DJSP), which is a complex dynamic optimization problem under uncertainty. We propose a DRL based method to learn dispatching policies for DJSP. Unlike existing DRL based dynamic scheduling methods that use a fixed number of dispatching rules as actions, our decision-making framework directly selects legitimate jobs, which is able to break the limitations imposed by priority dispatching rules. We design two training methods, including a gradient based algorithm with dense rewards, and an evolutionary strategy with sparse rewards. Extensive experiments show that our DRL method can learn high-quality DJSP dispatching policies, and can significantly outperform a state-of-the-art Genetic Programming (GP) based dispatching rule learning method.
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关键词
Deep Reinforcement Learning,Dynamic Job Shop Scheduling Problem,Evolutionary Strategy
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