Order Management and Completion Date Prediction of Manufacturing Job-Shop Based on Deep Learning

Abstract

To cope with the volatility of customer order demand, enterprises need to formulate a reasonable production plan based on customer demand for the completion period and their current manufacturing capacity. The existing studies have not fully considered the complex processing procedures, the features of manufacturing attributes, and the repetitive orders of stable consumers. To solve these problems, this paper explores the order management and completion date prediction of manufacturing job-shop based on deep learning. Specifically, the features of manufacturing attributes were extracted and used to predict the activities and completion time of different manufacturing tasks in order management. In addition, a deep learning prediction model was constructed based on a bidirectional long short-term memory network (BiLSTM) and self-attention mechanism, which completes the order management and completion date prediction.