图池化作为图神经网络中重要的组件,在获取图的多粒度信息的过程中扮演了重要角色。而当前的图池化操作均以平等地位看待数据点,普遍未考虑利用邻域内数据之间的偏序关系,从而造成图结构信息破坏。针对此问题,本文提出一种基于偏序关系...图池化作为图神经网络中重要的组件,在获取图的多粒度信息的过程中扮演了重要角色。而当前的图池化操作均以平等地位看待数据点,普遍未考虑利用邻域内数据之间的偏序关系,从而造成图结构信息破坏。针对此问题,本文提出一种基于偏序关系的多视图多粒度图表示学习框架(multi-view and multi-granularity graph representation learning based on partial order relationships,MVMGr-PO),它通过从节点特征视图、图结构视图以及全局视图对节点进行综合评分,进而基于节点之间的偏序关系进行下采样操作。相比于其他图表示学习方法,MVMGr-PO可以有效地提取多粒度图结构信息,从而可以更全面地表征图的内在结构和属性。此外,MVMGr-PO可以集成多种图神经网络架构,包括GCN(graph convolutional network)、GAT(graph attention network)以及GraphSAGE(graph sample and aggregate)等。通过在6个数据集上进行实验评估,与现有基线模型相比,MVMGr-PO在分类准确率上有明显提升。展开更多
The connection between production scheduling and transportation scheduling is getting closer in smart manufacturing system, and both of those problems are summarized as NP-hard problems. However, only a few studies ha...The connection between production scheduling and transportation scheduling is getting closer in smart manufacturing system, and both of those problems are summarized as NP-hard problems. However, only a few studies have considered them simultaneously. This paper solves the integrated production and transportation scheduling problem(IPTSP) in hybrid flow shops, which is an extension of the hybrid flow shop scheduling problem(HFSP). In addition to the production scheduling on machines, the transportation scheduling process on automated guided vehicles(AGVs)is considered as another optimization process. In this problem, the transfer tasks of jobs are performed by a certain number of AGVs. To solve it, we make some preparation(including the establishment of task pool, the new solution representation and the new solution evaluation), which can ensure that satisfactory solutions can be found efficiently while appropriately reducing the scale of search space. Then, an effective genetic tabu search algorithm is used to minimize the makespan. Finally, two groups of instances are designed and three types of experiments are conducted to evaluate the performance of the proposed method. The results show that the proposed method is effective to solve the integrated production and transportation scheduling problem.展开更多
文摘图池化作为图神经网络中重要的组件,在获取图的多粒度信息的过程中扮演了重要角色。而当前的图池化操作均以平等地位看待数据点,普遍未考虑利用邻域内数据之间的偏序关系,从而造成图结构信息破坏。针对此问题,本文提出一种基于偏序关系的多视图多粒度图表示学习框架(multi-view and multi-granularity graph representation learning based on partial order relationships,MVMGr-PO),它通过从节点特征视图、图结构视图以及全局视图对节点进行综合评分,进而基于节点之间的偏序关系进行下采样操作。相比于其他图表示学习方法,MVMGr-PO可以有效地提取多粒度图结构信息,从而可以更全面地表征图的内在结构和属性。此外,MVMGr-PO可以集成多种图神经网络架构,包括GCN(graph convolutional network)、GAT(graph attention network)以及GraphSAGE(graph sample and aggregate)等。通过在6个数据集上进行实验评估,与现有基线模型相比,MVMGr-PO在分类准确率上有明显提升。
基金Supported by National Key R&D Program of China (Grant No. 2019YFB1704603)National Natural Science Foundation of China (Grant Nos. U21B2029 and 51825502)。
文摘The connection between production scheduling and transportation scheduling is getting closer in smart manufacturing system, and both of those problems are summarized as NP-hard problems. However, only a few studies have considered them simultaneously. This paper solves the integrated production and transportation scheduling problem(IPTSP) in hybrid flow shops, which is an extension of the hybrid flow shop scheduling problem(HFSP). In addition to the production scheduling on machines, the transportation scheduling process on automated guided vehicles(AGVs)is considered as another optimization process. In this problem, the transfer tasks of jobs are performed by a certain number of AGVs. To solve it, we make some preparation(including the establishment of task pool, the new solution representation and the new solution evaluation), which can ensure that satisfactory solutions can be found efficiently while appropriately reducing the scale of search space. Then, an effective genetic tabu search algorithm is used to minimize the makespan. Finally, two groups of instances are designed and three types of experiments are conducted to evaluate the performance of the proposed method. The results show that the proposed method is effective to solve the integrated production and transportation scheduling problem.
