Workshop Order Remaining Completion Time Prediction Based on the Feature Selection and SSA-LSTM

doi:10.3901/JME.2024.20.388

Abstract

Abstract: The ever-changing demands of customers and fierce market competition have led to an increase in the number of products produced in the workshop, a decrease in batch sizes and a shortening of delivery times, which has led to an increasingly complex and unstable business process and manufacturing system environment, placing greater demands on the workshop's production control capabilities. Accurate forecasting of order remaining completion time(ORCT) allows for the timely detection of production schedule fluctuations and provides a basis for subsequent optimisation of the workshop production scheduling plan and process improvements. A big data-driven workshop ORCT prediction method is proposed to address the weak ability of traditional data analysis methods to utilise large-scale, long-time series manufacturing data in the complex manufacturing environment. A feature selection algorithm based on Pearson's correlation coefficient and regularisation is used to select key data from a huge amount of manufacturing data; a prediction method based on the improved sparrow search algorithm-long and short-term memory neural network model and the attention mechanism is designed to achieve fast and accurate prediction of ORCT in the workshop, where the prediction model is supported by the long and short-term memory neural network structure, which is optimised by the improved sparrow search algorithm; finally, a typical machine workshop is used as an example for verification, by comparing three common prediction methods, the experimental results show that the proposed method has obvious advantages in terms of accuracy and efficiency.

Key words: order remaining completion time, data-driven, feature selection, prediction model

CLC Number:

TP183

ZHU Haihua, WANG Jianjie, LI Fei, LIU Changchun, CAI Qixiang. Workshop Order Remaining Completion Time Prediction Based on the Feature Selection and SSA-LSTM[J]. Journal of Mechanical Engineering, 2024, 60(20): 388-402.

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