A machine learning approach applied to energy prediction in job shop environments

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dc.contributor.authorPEREIRA, M. S.
dc.contributor.authorFabio Lima
dc.contributor.authorOrcidhttps://orcid.org/0000-0002-5500-3191
dc.date.accessioned2022-01-12T21:56:51Z
dc.date.available2022-01-12T21:56:51Z
dc.date.issued2018
dc.description.abstract© 2018 IEEE.Energy efficiency has become a great challenge for manufacturing companies. Although it is possible to improve efficiency applying new and more efficient machines, decision makers tend to look for some less expensive alternatives. In this context, the adoption of more efficient strategies during the production planning can allow the reduction in energy consumption and associated emissions. Furthermore, the current reality of manufacturing companies, brought by Industry 4.0 concepts, requires more flexibility of production systems, thus, increasing complexity for machine rescheduling without compromising sustainable requirements. In this paper, we propose a method to predict total energy consumption in job shop systems applying machine learning techniques. Different schedules may result in different consumption rates. However, there is a nonlinear relationship between these targets. Therefore, an Artificial Neural Network (ANN) is applied for a quick estimation of total energy consumption. In order to validate the model, computational experiments, using digital manufacturing software tools, are performed on different job shop configurations to show the efficiency of the proposed model.
dc.description.firstpage2665
dc.description.lastpage2670
dc.identifier.citationPEREIRA, M. S.; LIMA F. A machine learning approach applied to energy prediction in job shop environments. Proceedings: IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society, P. 2665-2670, dec. 2018.
dc.identifier.doi10.1109/IECON.2018.8592763
dc.identifier.urihttps://repositorio.fei.edu.br/handle/FEI/3752
dc.relation.ispartofProceedings: IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society
dc.rightsAcesso Restrito
dc.titleA machine learning approach applied to energy prediction in job shop environments
dc.typeArtigo de evento
fei.scopus.citations4
fei.scopus.eid2-s2.0-85061559506
fei.scopus.subjectComputational experiment
fei.scopus.subjectJob-shop configuration
fei.scopus.subjectMachine learning approaches
fei.scopus.subjectMachine learning techniques
fei.scopus.subjectManufacturing companies
fei.scopus.subjectNon-linear relationships
fei.scopus.subjectReduction in energy consumption
fei.scopus.subjectTotal energy consumption
fei.scopus.updated2024-12-01
fei.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85061559506&origin=inward
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