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dc.contributor.authorTalavera, Alvaro
dc.contributor.authorLuna, Ana
dc.date.accessioned2024-11-25T13:11:16Z
dc.date.available2024-11-25T13:11:16Z
dc.date.issued2020
dc.identifier.urihttp://riull.ull.es/xmlui/handle/915/40204
dc.description.abstractIn this work, we integrate computational techniques based on machine learning (ML) and computational intelligence (CI) to conventional methodologies used in the Operational Research (OR) degree course for Engineers. That synergy between those techniques and methods allows students to deal with decision-making complex problems. The primary goals of this research work are to present potential interactions between the two computational fields and show some examples of them. This is a contribution to engineering education research where we present how ML techniques, such as neural networks, fuzzy logic, and reinforcement learning are integrated through applications in an OR course, being able to increase the approach of more complex problems in a simpler way compared to traditional OR methods. The current paper is a different proposal for OR courses that uses the symbiosis between mathematical models employing computer simulations, CI and different hybrid models.es_ES
dc.language.isoeses_ES
dc.relation.ispartofseriesIEEE Revista Iberoamericana de Tecnologias del Aprendizaje, vol. 15, no. 2;
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titleAprendizaje Automático: Una Contribución a la Investigación Operativaes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.identifier.doi10.1109/RITA.2020.2987700
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.subject.keywordoperational researches_ES
dc.subject.keywordmachine learninges_ES
dc.subject.keywordoptimizationes_ES
dc.subject.keywordhybrid modelses_ES
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_ES


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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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