| 英文标题 | Method to solve Job-Shop scheduling problem using deep recurrent neural network model with embedded pointer network |
| 作者英文名 | Ren Jianfeng, Ye Chunming |
| 机构英文名 | 1.School of Business,University of Shanghai for Science & Technology,Shanghai 200093,China;2.School of Computer & Information Engineering,Henan University of Economics & Law,Zhengzhou 450018,China |
| 英文摘要 | This paper proposed a data-driven Job-Shop scheduling algorithm. It derived the training samples from some benchmark instances and actual production data. It constructed the feature data of the samples using the feature function and then normalized. It constituted the tag data by the mapping relations between the scheduling tasks and the corresponding scheduling rules. This paper embedded a pointer network into the main framework of the LSTM recurrent neural network model so that the workpiece with the highest probability in the current sequence would be passed to the buffer at first, which improved the training speed and training quality of the neural network. The result of an experiment shows that the proposed model is effective in solving Job-Shop scheduling problem after training. This study provides a new idea for solving the Job-Shop scheduling problem. |
| 英文关键词 | long short-term memory(LSTM); pointer network; attention mechanism; Job-Shop scheduling |