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Forecasting Future Product Sequences To Be Processed In Tire Production Using Deep Learning Technique.
- Source :
- Procedia Computer Science; 2023, Vol. 219, p354-361, 8p
- Publication Year :
- 2023
-
Abstract
- Production sequencing methodology using DeepLearning Seq2Seq-LSTM is applied to a tire production case study in Quebec, Canada. Production and demand data are used to predict the most likely product sequences to operate. The comparison of 4 forecasting models, differing in consideration of demand and a statistical component, leads to nearly 70% of good prediction when all machines are studied and 10 production scenarios are considered. This performance reaches 92% for a specific class of machines. The analysis of the forecasts by class of machine allows highlighting 2 factors influencing the performance of the models, namely the ratio of product/machine by class and the total number of available records. The forecasts of possible production scenarios can then be used in a digital twin to evaluate a reasonable number of options and develop a decision support system for production sequencing. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 18770509
- Volume :
- 219
- Database :
- Supplemental Index
- Journal :
- Procedia Computer Science
- Publication Type :
- Academic Journal
- Accession number :
- 162590491
- Full Text :
- https://doi.org/10.1016/j.procs.2023.01.300