<?xml version="1.0" encoding="UTF-8"?><?xml-stylesheet type="text/xsl" href="static/style.xsl"?><OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-04-14T14:32:20Z</responseDate><request verb="GetRecord" identifier="oai:uvadoc.uva.es:10324/75263" metadataPrefix="mods">https://uvadoc.uva.es/oai/request</request><GetRecord><record><header><identifier>oai:uvadoc.uva.es:10324/75263</identifier><datestamp>2025-04-02T08:53:27Z</datestamp><setSpec>com_10324_38</setSpec><setSpec>col_10324_852</setSpec></header><metadata><mods:mods xmlns:mods="http://www.loc.gov/mods/v3" xmlns:doc="http://www.lyncode.com/xoai" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-1.xsd">
<mods:name>
<mods:namePart>Redondo García, Elena</mods:namePart>
</mods:name>
<mods:extension>
<mods:dateAvailable encoding="iso8601">2025-03-06T15:03:45Z</mods:dateAvailable>
</mods:extension>
<mods:extension>
<mods:dateAccessioned encoding="iso8601">2025-03-06T15:03:45Z</mods:dateAccessioned>
</mods:extension>
<mods:originInfo>
<mods:dateIssued encoding="iso8601">2024</mods:dateIssued>
</mods:originInfo>
<mods:identifier type="uri">https://uvadoc.uva.es/handle/10324/75263</mods:identifier>
<mods: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.</mods:abstract>
<mods: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.</mods:abstract>
<mods:language>
<mods:languageTerm>eng</mods:languageTerm>
</mods:language>
<mods:accessCondition type="useAndReproduction">info:eu-repo/semantics/openAccess</mods:accessCondition>
<mods:accessCondition type="useAndReproduction">http://creativecommons.org/licenses/by-nc-nd/4.0/</mods:accessCondition>
<mods:accessCondition type="useAndReproduction">Attribution-NonCommercial-NoDerivatives 4.0 Internacional</mods:accessCondition>
<mods:titleInfo>
<mods:title>Influence of AI-Generated Data in the  simulation of an industrial process</mods:title>
</mods:titleInfo>
<mods:genre>info:eu-repo/semantics/bachelorThesis</mods:genre>
</mods:mods></metadata></record></GetRecord></OAI-PMH>