<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>1794-1237</journal-id>
<journal-title><![CDATA[Revista EIA]]></journal-title>
<abbrev-journal-title><![CDATA[Rev.EIA.Esc.Ing.Antioq]]></abbrev-journal-title>
<issn>1794-1237</issn>
<publisher>
<publisher-name><![CDATA[Escuela de ingenieria de Antioquia]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1794-12372019000100057</article-id>
<article-id pub-id-type="doi">10.24050/reia.v16i31.867</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Clasificador bayesiano de dos clases para seleccionar la mejor regla de prioridad en un problema Job Shop: Open Shop]]></article-title>
<article-title xml:lang="en"><![CDATA[A Two-Class Bayesian Classifier to Select the Best Priority Rule in a Job Shop: Open Shop Scheduling Problem]]></article-title>
<article-title xml:lang="pt"><![CDATA[Classificador bayesiano de duas classes para selecionar a melhor regla de prioridade em um problema Job Shop: Open Shop]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Castrillón]]></surname>
<given-names><![CDATA[Omar Danilo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Sarache]]></surname>
<given-names><![CDATA[William Ariel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Herrera]]></surname>
<given-names><![CDATA[Santiago Ruiz]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Nacional de Colombia Facultad de ingeniería y arquitectura ]]></institution>
<addr-line><![CDATA[Manizales ]]></addr-line>
<country>Colombia</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2019</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2019</year>
</pub-date>
<volume>16</volume>
<numero>31</numero>
<fpage>57</fpage>
<lpage>64</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S1794-12372019000100057&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S1794-12372019000100057&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S1794-12372019000100057&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen El objetivo de este trabajo es seleccionar, por medio de un clasificador bayesiano de dos clases, la mejor regla de prioridad que puede ser aplicada en un problema Job Shop: Open Shop. En una primera fase se expone el diseño del clasificador, entrenado con 300 problemas generados aleatoriamente. En 150 de ellos, la mejor regla de prioridad para secuenciarlos fue FIFO (First in First Out) y en los restantes fue la regla LPT (Long Process Time). En una segunda fase, un conjunto de 300 problemas diferentes, con las mismas características de la primera fase, fueron generados aleatoriamente. Estos problemas fueron clasificados previamente (sin secuenciarlos) por medio la técnica bayesiana propuesta. Los resultados demuestran que en el 96% de los casos, el clasificador propuesto logra identificar la mejor regla de prioridad para secuenciar pedidos.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract The aim of the present paper to select, through a two-class Bayesian classifier, the best priority rule to solve a Job Shop: Open Shop scheduling problem. In a first phase, the design of the classifier, trained with 300 randomly problems, is exposed. In 150 of them, the best priority rule for sequencing was FIFO (First In First Out) while in the rest was the LPT (Long Process Time). In a second phase, a set of 300 different problems, with the same characteristics of the first phase, were randomly generated. These problems were classified previously (without sequencing them) through the proposed Bayesian technique. The results show that, in 96% of cases, the proposed classifier identifies the best priority rule to sequence the orders.]]></p></abstract>
<abstract abstract-type="short" xml:lang="pt"><p><![CDATA[Resumo O objetivo deste trabalho é seleccionar, por meio de um classificador bayesiano de duas clases, a melhor regra de prioridade que pode-se aplicar em um problema Job Shop: Open Shop. Na primeira fase expõe-se o design do classifi-cador, treinado com 300 problemas gerados aleatoriamente. Em 150, a melhor regra de prioridade para os sequenciar foi FIFO (First in First out), e nos outros foi LPT (Long Process Time). Na segunda fase, um conjunto de 300 problemas diferentes, com as mesmas características da primeira fase, foram gerados aleatoriamente. Esses problemas foram classificados previamente (sem sequenciar) por meio da técnica bayesiana proposta. Os resultados mostraram que no 96% dos casos, o classificador proposto logrou identificar a melhor regra de prioridade para sequenciar os pedidos.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Programación de la producción]]></kwd>
<kwd lng="es"><![CDATA[Reglas de Prioridad]]></kwd>
<kwd lng="es"><![CDATA[Clasificador Bayesiano]]></kwd>
<kwd lng="es"><![CDATA[Job Shop: Open Shop]]></kwd>
<kwd lng="en"><![CDATA[Production scheduling]]></kwd>
<kwd lng="en"><![CDATA[Priority Rules]]></kwd>
<kwd lng="en"><![CDATA[Bayesian classifier]]></kwd>
<kwd lng="en"><![CDATA[Job Shop: Open Shop]]></kwd>
<kwd lng="pt"><![CDATA[Programação de produção]]></kwd>
<kwd lng="pt"><![CDATA[regras de prioridade]]></kwd>
<kwd lng="pt"><![CDATA[Classificador Bayesiano]]></kwd>
<kwd lng="pt"><![CDATA[Job Shop: Open Shop]]></kwd>
</kwd-group>
</article-meta>
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