<?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>2007-7467</journal-id>
<journal-title><![CDATA[RIDE. Revista Iberoamericana para la Investigación y el Desarrollo Educativo]]></journal-title>
<abbrev-journal-title><![CDATA[RIDE. Rev. Iberoam. Investig. Desarro. Educ]]></abbrev-journal-title>
<issn>2007-7467</issn>
<publisher>
<publisher-name><![CDATA[Centro de Estudios e Investigaciones para el Desarrollo Docente A.C.]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2007-74672025000200928</article-id>
<article-id pub-id-type="doi">10.23913/ride.v16i31.2519</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Optimización Robusta e Ingeniería Predictiva Vs. Arreglos Ortogonales, Metodología de Superficie de Respuesta, Diseño Central Compuesto y Respuesta Dual: Caso comparativo]]></article-title>
<article-title xml:lang="en"><![CDATA[Robust Optimization and Predictive Engineering Vs. Orthogonal Arrays, Response Surface Methodology, Central Composite Design and Dual Response: Comparative Case]]></article-title>
<article-title xml:lang="pt"><![CDATA[Otimização Robusta e Engenharia Preditiva vs. Matrizes Ortogonais, Metodologia de Superfície de Resposta, Projeto Central Composto e Resposta Dupla: Caso Comparativo]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Rodríguez-Medina]]></surname>
<given-names><![CDATA[Manuel. A.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Gómez Martínez]]></surname>
<given-names><![CDATA[Gabriel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Herrera Ríos]]></surname>
<given-names><![CDATA[Ericka Berenice]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Poblano Ojinaga]]></surname>
<given-names><![CDATA[Eduardo Rafael]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Tecnológico Nacional de México Instituto Tecnológico de Ciudad Juárez ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>México</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Tecnológico Nacional de México Instituto Tecnológico de Ciudad Juárez ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>México</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Tecnológico Nacional de México Instituto Tecnológico de Ciudad Juárez ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>México</country>
</aff>
<aff id="Af4">
<institution><![CDATA[,Tecnológico Nacional de México Instituto Tecnológico de Ciudad Juárez ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>México</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2025</year>
</pub-date>
<volume>16</volume>
<numero>31</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S2007-74672025000200928&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S2007-74672025000200928&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S2007-74672025000200928&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen La Optimización Robusta es una herramienta estadística matemática utilizada para fortalecer las características críticas de un producto o sistema cuando estos operan bajo condiciones adversas, afectando su desempeño y/o rendimiento. Se ha llevado a cabo un estudio de la variación presente en factores o variables tales como: temperatura, humedad, voltaje, polvo, entre otros., con mayor incidencia e impacto en los resultados, para posteriormente identificar los mejores niveles de operación con la mínima variación presente en las condiciones de ambiente adverso (estrés). Además, la Ingeniería Predictiva (IP), es una herramienta probabilística matemática que se ha utilizado para modelar el comportamiento de cierta característica de un producto o sistema, utilizando una función de probabilidad que permita predecir una ocurrencia o porcentaje de desempeño frente a determinadas condiciones operantes en el sistema. Este tipo de herramientas son utilizadas por ingenieros, científicos e investigadores para proponer soluciones de peso a problemas relacionados al diseño y manufactura de productos, aunque no se descarta su uso para el diseño de sistemas tales como Software, Redes, Logística u otros servicios. Sin lugar a duda, los profesionales en el campo de la ingeniería deberán estar familiarizados con las técnicas de optimización y predicción como estas, por lo que se requiere que sean examinadas en detalle. El objetivo de este trabajo es presentar las ventajas de estas de la Optimización Robusta e Ingeniería Predictiva en comparación de MSR (Metodología de Superficie de Respuesta). Además, la comparación del DCC (Diseño Central Compuesto) y RD (Respuesta Dual) a través de un caso de aplicación.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Robust Optimization is a mathematical statistical tool to strengthen the critical characteristics of a product or system when they operate under adverse conditions that could affect their performance. This is achieved by studying the variation in variables such as temperature, humidity, voltage, dust, and others of greater incidence and how they impact the result, to later identify the best levels of operation with the minimum variation present in adverse environmental conditions (stress). In addition, Predictive Engineering is a mathematical probabilistic tool that has been used to model the behavior of a certain characteristic of a product or system using a probability model and predict an occurrence or percentage of performance against certain operating conditions in the system. These types of tools are used by engineers, scientists and researchers to propose robust solutions to problems related mainly to the design and manufacture of products, although their use for the design of systems such as Software, Networks, Logistics, or others services is not ruled out. Undoubtedly, professionals in the field of engineering will need to be familiar with optimization and prediction techniques such as these, so they need to be examined in detail. The objective of this paper is aims to compare Robust Optimization and Predictive Engineering with MSR, DCC, and RD, through a case study.]]></p></abstract>
<abstract abstract-type="short" xml:lang="pt"><p><![CDATA[Resumo A Otimização Robusta é uma ferramenta estatística matemática usada para fortalecer as características críticas de um produto ou sistema quando operam em condições adversas, afetando seu desempenho e/ou rendimento. Foi realizado um estudo da variação presente em fatores ou variáveis &#8203;&#8203;como temperatura, umidade, tensão, poeira, entre outros, com maior incidência e impacto nos resultados, para posteriormente identificar os melhores níveis operacionais com a mínima variação presente em condições ambientais adversas (estresse). Além disso, a Engenharia Preditiva (PE) é uma ferramenta matemática probabilística que tem sido usada para modelar o comportamento de uma determinada característica de um produto ou sistema, usando uma função de probabilidade que permite prever uma ocorrência ou porcentagem de desempenho diante de certas condições operacionais no sistema. Esses tipos de ferramentas são usados &#8203;&#8203;por engenheiros, cientistas e pesquisadores para propor soluções significativas para problemas relacionados ao projeto e fabricação de produtos, embora seu uso no projeto de sistemas como software, redes, logística ou outros serviços não seja descartado. Sem dúvida, os profissionais de engenharia devem estar familiarizados com técnicas de otimização e predição como essas, portanto, requerem um exame detalhado. O objetivo deste artigo é apresentar as vantagens da Otimização Robusta e da Engenharia Preditiva em comparação com a Metodologia de Superfície de Resposta (RSM). Além disso, compara-se o DCC (Projeto Composto Central) e o RD (Resposta Dupla) por meio de um caso de aplicação.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Producto Robusto]]></kwd>
<kwd lng="es"><![CDATA[Superficie de Respuesta]]></kwd>
<kwd lng="es"><![CDATA[Respuesta Dual]]></kwd>
<kwd lng="es"><![CDATA[Modelo Probabilístico]]></kwd>
<kwd lng="es"><![CDATA[Índice de Capacidad del proceso (Cpk)]]></kwd>
<kwd lng="es"><![CDATA[Rendimiento]]></kwd>
<kwd lng="en"><![CDATA[Robust Product]]></kwd>
<kwd lng="en"><![CDATA[Response Surface]]></kwd>
<kwd lng="en"><![CDATA[Dual Response]]></kwd>
<kwd lng="en"><![CDATA[Probabilistic Model]]></kwd>
<kwd lng="en"><![CDATA[Process Capability Index (Cpk)]]></kwd>
<kwd lng="en"><![CDATA[Performance]]></kwd>
<kwd lng="pt"><![CDATA[Produto Robusto]]></kwd>
<kwd lng="pt"><![CDATA[Superfície de Resposta]]></kwd>
<kwd lng="pt"><![CDATA[Resposta Dupla]]></kwd>
<kwd lng="pt"><![CDATA[Modelo Probabilístico]]></kwd>
<kwd lng="pt"><![CDATA[Índice de Capacidade do Processo (Cpk)]]></kwd>
<kwd lng="pt"><![CDATA[Desempenho]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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