Currently,most refinery crude oil scheduling studies adopt static scheduling schemes based on mathematical programming,which cannot adjust and optimize according to environmental change in real-time.This paper established a dynamic real-time scheduling decision model subject to refinery production constraints and designed the corresponding agent interaction environment.The soft actor-critic(SAC)algorithm in deep reinforcement learning solved the model.Firstly,the crude oil resource scheduling problem was transformed into a Markov decision process,and a deep reinforcement learning algorithm based on SAC was proposed to simultaneously determine discrete decisions such as transmission target and continuous decisions such as transmission speed in the scheduling process.Extensive experimental results showed that the strategy learned by the algorithm has better usability,which effectively improved the decision-making efficiency of the algorithm and effectively controlled the influence range of random events on the overall decision-making compared with the baseline algorithm.This algorithm can provide new ideas for rapid decision-making of crude oil storage and transportation scheduling in coastal refineries.
关键词
炼厂原油储运/资源调度/深度强化学习/软演员-评论家
Key words
refinery crude oil storage and transportation/resource scheduling/deep reinforcement learning/soft actor-critic(SAC)
维普
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