Forecasting future product sequences to be processed in tire production using deep learning technique

Ambre Dupuis, Camélia Dadouchi, Bruno Agard, Robert Pellerin

Paper (2022)

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Department:
Department of Mathematics and Industrial Engineering
Research Center:
LID - Laboratoire en intelligence des données
PolyPublie URL:
https://lx-publications2.polymtl.ca/54099/
Conference Title:
International Conference on ENTERprise Information Systems (CENTERIS 2022)
Conference Location:
Lisbon, Portugal
Conference Date(s):
2022-11-09 - 2022-11-11
Journal Title :
Procedia Computer Science ( vol. 219 )
Publisher:
Elsevier
DOI:
10.1016/j.procs.2023.01.300
Official URL:
https://doi.org/10.1016/j.procs.2023.01.300
OAI:
oai:publications.polymtl.ca:54099
ORCID
Date Deposited:
10 Jul 2023 16:30
Last Modified:
02 Oct 2026 23:49
Cite in APA 7:
Dupuis, A., Dadouchi, C., Agard, B., & Pellerin, R. (2022, November). Forecasting future product sequences to be processed in tire production using deep learning technique [Paper]. International Conference on ENTERprise Information Systems (CENTERIS 2022), Lisbon, Portugal. Published in Procedia Computer Science, 219. https://doi.org/10.1016/j.procs.2023.01.300

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