<?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>0121-750X</journal-id>
<journal-title><![CDATA[Ingeniería]]></journal-title>
<abbrev-journal-title><![CDATA[ing.]]></abbrev-journal-title>
<issn>0121-750X</issn>
<publisher>
<publisher-name><![CDATA[Universidad Distrital Francisco José de Caldas]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0121-750X2023000200201</article-id>
<article-id pub-id-type="doi">10.14483/23448393.18961</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Resolución del problema integrado de enrutamiento y gestión de inventarios con múltiples vehículos mediante programación lineal entera mixta]]></article-title>
<article-title xml:lang="en"><![CDATA[Solving the Multi-Vehicle Inventory Routing and Management Integrated Problem Using Mixed-Integer Linear Programming]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Hincapié-Londoño]]></surname>
<given-names><![CDATA[Frank Alejandro]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[García-Guevara]]></surname>
<given-names><![CDATA[Jhonatan Stiven]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Toro-Ocampo]]></surname>
<given-names><![CDATA[Eliana Mirledy]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Tecnológica de Pereira  ]]></institution>
<addr-line><![CDATA[Pereira Risaralda]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Tecnológica de Pereira  ]]></institution>
<addr-line><![CDATA[Pereira Risaralda]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad Tecnológica de Pereira  ]]></institution>
<addr-line><![CDATA[Pereira Risaralda]]></addr-line>
<country>Colombia</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>08</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>08</month>
<year>2023</year>
</pub-date>
<volume>28</volume>
<numero>2</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S0121-750X2023000200201&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S0121-750X2023000200201&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S0121-750X2023000200201&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen  Contexto:  El control y distribución de inventarios son procesos claves en la pérdida tanto de eficiencia como de eficacia en el campo de la logística, a nivel global. Según el Departamento Nacional de Planeación de Colombia, en el año 2020, los rubros de transporte, almacenamiento e inventarios correspondieron al 73,9 % del costo logístico total. La resolución del problema de enrutamiento inventario con múltiples vehículos (IRP) representa una alternativa para hacer que los tiempos de ciclo sean más cortos, más flexibles y menos costosos.  Método:  En este artículo se describen y comparan modelos matemáticos de la literatura para el problema base, adaptándolo a su variante con múltiples vehículos y resolviéndolo a través modelos de programación lineal entera mixta, mediante la solución de instancias de baja y media complejidad matemática, planteando dos maneras de gestionar el inventario y tres formas de eliminar la creación de sub-tours. Para obtener los resultados se utiliza el software AMPL en un computador con procesador Intel Core i5-5200U CPU @ 2.2 GHz y 4 GB de RAM, considerando un tiempo máximo de ejecución de una hora.  Resultados:  El modelo de flujos muestra el mejor desempeño en cuanto a tiempos computacionales y calidad de la respuesta con respecto a la política de máximo nivel (ML), y la variante MTZ es el segundo mejor modelo. Por último, la variación general reporta mayores tiempos de ejecución y valores GAP más altos. Los modelos presentan buen desempeño para instancias de pequeño y mediano tamaño.  Conclusiones:  En esta investigación se presenta una metodología general que puede ser adaptada a diferentes aplicaciones del problema integrado de inventarios y enrutamiento con múltiples vehículos. Se comprobó que las respuestas generadas son de buena calidad, destacando la eliminación de sub-tours por medio del modelo de flujos y la gestión de inventarios bajo la política ML. Los trabajos futuros deben encaminarse en busca de nuevas alternativas a la optimización exacta, tales como las heurísticas o metaheurísticas, que permitan estar lo más cerca posible del óptimo en tiempos de computación más cortos. Además, se podrían considerar demandas estocásticas y el manejo de productos perecederos, entre otros.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract  Context:  Inventory control and distribution are key processes in the loss of both efficiency and effectiveness in the field of logistics at the global level. According to the Colombian National Planning Department, for the year 2020, the items of transportation, storage, and inventory account for 73,9 % of the total logistics costs. Solving the multi-vehicle Inventory Routing Problem (IRP) is an alternative to making cycle times shorter, more flexible, and less expensive.  Method:  This article describes and compares mathematical models in the literature for the basic problem, adapting it to its multi-vehicle variant and solving it by means of mixed-integer programming models, via the solution of instances of low and medium mathematical complexity, proposing two ways to manage the inventory and three ways to eliminate the creation of sub-tours. To obtain the results, the AMPL software is used in a computer with an Intel Core i5-5200U CPU@ 2.2 GHz processor and 4 GB RAM, considering a maximum runtime of one hour.  Results:  The flow model shows the best performance in terms of computational times and response quality regarding the maximum level (ML) policy, and the MTZ variant is the second-best model. Finally, the overall variation exhibits longer execution times and higher GAP values. The models perform well for small- and medium-sized instances.  Conclusions:  This research presents a general methodology that can be adapted to different applications of the integrated problem of inventory and routing with multiple vehicles. It was verified that the responses generated are of good quality, highlighting the elimination of sub-tours through the flow model and inventory management under the ML policy. Future work should be directed towards finding new alternatives to exact optimization, such as heuristics and metaheuristics, which allow being as close as possible to the optimum in shorter computation times. In addition, stochastic demands could be considered, as well as the handling of perishable products, among others.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[gestión de inventarios]]></kwd>
<kwd lng="es"><![CDATA[problema de enrutamiento de inventario]]></kwd>
<kwd lng="es"><![CDATA[logística]]></kwd>
<kwd lng="es"><![CDATA[modelado matemático]]></kwd>
<kwd lng="es"><![CDATA[enrutamiento de vehículos.]]></kwd>
<kwd lng="en"><![CDATA[inventory management]]></kwd>
<kwd lng="en"><![CDATA[inventory routing problem]]></kwd>
<kwd lng="en"><![CDATA[logistics]]></kwd>
<kwd lng="en"><![CDATA[mathematical modeling]]></kwd>
<kwd lng="en"><![CDATA[vehicle routing.]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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