An efficient method for solving large-scale open shop scheduling problem based on Horovod-GPU and improved graph attention network

Abstract

The open shop scheduling problem (OSSP) involves complex processing constraints and a large number of job-machine combinations, which leads to an exponential increase in the solution space. For large-scale OSSP in real-world industrial productions, traditional methods struggle to provide satisfactory optimization results within a limited time. Therefore, an efficient method for solving large-scale OSSP through improved graph attention network based on link prediction (IGAT-LP) and Horovod-GPU is proposed. Firstly, an open shop scheduling (OSS) model based on IGAT-LP is designed to make full use of the feature information of operation nodes in OSSP. The model employs the graph attention network (GAT) structure to capture dependencies between tasks, learns global information through a multi-head attention mechanism, and predicts the optimal matching order between operations and machines. Secondly, a distributed parallelization method for the OSS model based on IGAT-LP is proposed. The distributed training capability of Horovod-GPU platform is fully utilized to expand the model training across multiple GPU nodes, significantly improving training efficiency. Finally, extensive experiments are conducted to analyze the effectiveness of the proposed method. The experimental results verify the superiority of the proposed method for solving large-scale OSSP instances. Moreover, the method significantly enhances the training performance of the OSS model based on IGAT-LP.