College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310027, China.
College of Science, Changchun University of Science and Technology, Changchun 130022, China.
Comput Intell Neurosci. 2019 Jul 10;2019:7172842. doi: 10.1155/2019/7172842. eCollection 2019.
In this paper, a hybrid deep neural network scheduler (HDNNS) is proposed to solve job-shop scheduling problems (JSSPs). In order to mine the state information of schedule processing, a job-shop scheduling problem is divided into several classification-based subproblems. And a deep learning framework is used for solving these subproblems. HDNNS applies the convolution two-dimensional transformation method (CTDT) to transform irregular scheduling information into regular features so that the convolution operation of deep learning can be introduced into dealing with JSSP. The simulation experiments designed for testing HDNNS are in the context of JSSPs with different scales of machines and jobs as well as different time distributions for processing procedures. The results show that the MAKESPAN index of HDNNS is 9% better than that of HNN and the index is also 4% better than that of ANN in ZLP dataset. With the same neural network structure, the training time of the HDNNS method is obviously shorter than that of the DEEPRM method. In addition, the scheduler has an excellent generalization performance, which can address large-scale scheduling problems with only small-scale training data.
本文提出了一种混合深度神经网络调度器（HDNNS）来解决作业车间调度问题（JSSP）。为了挖掘调度处理的状态信息，将作业车间调度问题划分为几个基于分类的子问题，并使用深度学习框架来解决这些子问题。HDNNS 应用卷积二维变换方法（CTDT）将不规则的调度信息转换为规则的特征，以便将深度学习的卷积操作引入到处理 JSSP 中。为了测试 HDNNS 而设计的模拟实验是在机器和作业规模不同以及处理过程时间分布不同的 JSSP 背景下进行的。结果表明，与 HNN 相比，HDNNS 的 MAKESPAN 指标提高了 9%，在 ZLP 数据集上也比 ANN 提高了 4%。在相同的神经网络结构下，HDNNS 方法的训练时间明显短于 DEEPRM 方法。此外，调度器具有出色的泛化性能，可以仅使用小规模训练数据解决大规模调度问题。
