Scheduling algorithm for refinery crude oil storage and transportation based on SAC

被引：0
|
作者：
Ma N. [1 ]
Li H. [1 ]
Liu H. [2 ,3 ]
Yang L. [2 ,3 ]
机构：
[1] School of Information Science and Engineering, China University of Petroleum, Beijing
[2] Petrochina Planning and Engineering Institute, Beijing
[3] Key Laboratory of Oil Gas Business Chain Optimization, CNPC, Beijing
来源：
Huagong Jinzhan/Chemical Industry and Engineering Progress | / 43卷 / 03期
关键词：
deep reinforcement learning; refinery crude oil storage and transportation; resource scheduling; soft actor-critic (SAC);
D O I：
10.16085/j.issn.1000-6613.2023-1498
中图分类号：
学科分类号：
摘要：
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. © 2024 Chemical Industry Press Co., Ltd.. All rights reserved.
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页码：1167 / 1177
页数：10
相关论文
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