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<dc:title>Influence of AI-Generated Data in the  simulation of an industrial process</dc:title>
<dc:creator>Redondo García, Elena</dc:creator>
<dc:contributor>Gento Municio, Ángel Manuel</dc:contributor>
<dc:contributor>Sommer, Lutz</dc:contributor>
<dc:contributor>Universidad de Valladolid. Escuela de Ingenierías Industriales</dc:contributor>
<dcterms:abstract>Este trabajo se centra en analizar los efectos del uso de herramientas de machine &#xd;
learning en un problema concreto de logística. Para ello se ha creado un modelo &#xd;
logístico en el que existen cinco tipos de productos que pasan por ciertas fases antes &#xd;
de ser distribuidos a sus respectivos centros específicos. Las fases en las que &#xd;
consiste el problema son: recepción, almacenamiento, preparado de pedidos y &#xd;
distribución. Se ha utilizado el software Witness para realizar una simulación del &#xd;
sistema. Al principio se han establecido parámetros aleatorios para recopilar datos &#xd;
que posteriormente nos sirvan para alimentar la memoria de la inteligencia artificial &#xd;
utilizada para el análisis. Para finalizar, se han integrado todos los datos en el &#xd;
programa Orange Data Mining. Gracias al diagrama de flujo establecido, se ha hecho &#xd;
que los datos sean evaluados y mediante un código se busque la combinación &#xd;
óptima para la gestión de la simulación.</dcterms:abstract>
<dcterms:abstract>This work focuses on analyzing the effects of using machine learning tools in a &#xd;
specific logistics problem. To achieve this, a logistics model has been created in &#xd;
which there are five types of products that go through certain phases before being &#xd;
distributed to their respective specific centers. The phases involved in the problem &#xd;
are reception, storage, order preparation and distribution. The Witness software was &#xd;
used to simulate the system. Initially, random parameters were set to collect data &#xd;
that would later be used to feed the memory of the artificial intelligence used for the &#xd;
analysis. Finally, all the data was integrated into the Orange Data Mining program. &#xd;
Thanks to the established flow diagram, the data was evaluated and through a code, &#xd;
the optimal combination for managing the simulation was sought.</dcterms:abstract>
<dcterms:dateAccepted>2025-03-06T15:03:45Z</dcterms:dateAccepted>
<dcterms:available>2025-03-06T15:03:45Z</dcterms:available>
<dcterms:created>2025-03-06T15:03:45Z</dcterms:created>
<dcterms:issued>2024</dcterms:issued>
<dc:type>info:eu-repo/semantics/bachelorThesis</dc:type>
<dc:identifier>https://uvadoc.uva.es/handle/10324/75263</dc:identifier>
<dc:language>eng</dc:language>
<dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
<dc:rights>http://creativecommons.org/licenses/by-nc-nd/4.0/</dc:rights>
<dc:rights>Attribution-NonCommercial-NoDerivatives 4.0 Internacional</dc:rights>
</qdc:qualifieddc></metadata></record></GetRecord></OAI-PMH>