面向人机物三元数据的热轧调度问题研究
作者:
作者单位:

同济大学

作者简介:

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中图分类号:

TP18

基金项目:

科技创新2030新一代人工智能重大项目课题“数据驱动的人机物三元协同决策与优化” (2018AAA0101801)


Research on hot rolling scheduling problem oriented to Human-Cyber-Physical Data
Author:
Affiliation:

Tongji University

Fund Project:

Project supported by the National Science and Technology Innovation 2030 Next-Generation Artificial Intelligence Major Project,

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    摘要:

    随着钢铁行业的数字化发展其订单逐渐趋于多样化和随机化,这对热轧调度模型的适应性和灵活性等提出了新的要求。针对热轧调度问题,当前主流的方法是启发式算法,其存在两个问题,一是没有考虑数据的组织表示,二是此类算法具有很强的针对性,当问题发生很小的改变就需要进行复杂的参数调整,相比之下机器学习具有更好的适应性和灵活性,故本文采用本体进行人机物三元数据的组织表示,首次提出了指针网络+强化学习的热轧调度求解方法。采用指针网络来学习序列到序列的映射,同时为解决指针网络训练困难和性能不高等问题,通过Actor-Critic网络进行训练,提高模型的准确性和收敛速度。最后,通过设计相应的实验方案对算法的有效性和性能进行了仿真并和LK-H的局部搜索算法进行了对比,进一步验证了该方法的有效性。

    Abstract:

    With the digital development of the steel industry, the orders become Multiple species and random change, which puts forward new requirements for the adaptability and flexibility of the hot-rolling scheduling model. For the hot rolling scheduling problem, the current mainstream method is heuristic algorithm, which has two problems. One is that it does not consider the organizational representation of data; the other is that this kind of algorithm has strong pertinence. When the problem changes very little, it needs complex parameter adjustment. Compared with machine learning, it has better adaptability and flexibility. Therefore, this paper uses ontology to represent the organization of Human-Cyber-Physical data, and puts forward a hot rolling scheduling solution method of pointer network + reinforcement learning for the first time. The pointer network is used to learn the mapping from sequence to sequence. In order to solve the problems of the pointer network training difficulty and low performance, the actor critical network is used to improve the accuracy and convergence speed of the model. Finally, the effectiveness and performance of the algorithm are simulated by designing the corresponding experimental scheme and compared with lk-h"s local search algorithm to further verify the effectiveness of the method.

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历史
  • 收稿日期:2020-05-11
  • 最后修改日期:2021-07-09
  • 录用日期:2020-09-08
  • 在线发布日期: 2020-10-02
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