<?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>2007-0705</journal-id>
<journal-title><![CDATA[Nova scientia]]></journal-title>
<abbrev-journal-title><![CDATA[Nova scientia]]></abbrev-journal-title>
<issn>2007-0705</issn>
<publisher>
<publisher-name><![CDATA[Universidad de La Salle Bajío A. C., Coordinación de Investigación]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2007-07052016000200340</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Case study: Simulated annealing for improving the educational timetable]]></article-title>
<article-title xml:lang="es"><![CDATA[Estudio de caso: Recocido simulado para mejorar el calendario educativo]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Sánchez-Partida]]></surname>
<given-names><![CDATA[Diana]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Baquela]]></surname>
<given-names><![CDATA[Enrique Gabriel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Mora-Vargas]]></surname>
<given-names><![CDATA[Jaime]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Smith]]></surname>
<given-names><![CDATA[Neale R.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,ITESM School of Engineering and Sciences ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Furgens Research  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Argentina</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>00</month>
<year>2016</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>00</month>
<year>2016</year>
</pub-date>
<volume>8</volume>
<numero>17</numero>
<fpage>340</fpage>
<lpage>360</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S2007-07052016000200340&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S2007-07052016000200340&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S2007-07052016000200340&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract  Introduction: On occasions, Combinatorial Optimization Problems (COP), like the University Course Timetabling Problem (CTTP), can be solved using Operational Research (OR) techniques; however, when the problem increases in size, finding a solution becomes more complex. This type of problem is NP-hard, requiring procedures like metaheuristic methods in order to solve the problem. This paper confronts a real world situation in Mexico concerning the Curriculum-Based Timetabling Problem (CB-CTT). Each institution have their own operationals rules due to the modeling of the problema is unique because preserve their own characteristics. First, as part of the contribution to the solution of the problem, it was implemented a Mediation Software (MS) in order to organize the raw data and eliminate a hard constraint related to curricula. Subsequently, the problem handle here was split into five instances in accordance to the courses that share the same physical space, which was solved using a typical Simulated Annealing (SA) metaheuristic. In addition, The problem was satisfactorily solved, assigning 9620 lectures in 174.5 hours approximatly, providing a solution without partitioning the problem into two subproblems, impacting positivly reducing the labor time, and providing a feasible and without errors educational timetable to the whole university.  Method: The Simulated Annealing (SA) algorithm is a meta-heuristic search for global optimization problems; the overall objective of such algorithms is to find a good approximation to the optimal value of a function in a large search space. This value is called "global or local optimum".The name and inspiration comes from the process of annealing the steel and ceramics, a technique that involves heating and then slowly cooling the material to vary its physical properties. The heat causes the atoms increase their energy and can thus move from their initial (a local minimum energy) positions; slow cooling gives them more likely to recrystallize in configurations with lower energy than the initial (minimum overall) .The method was independently described by Scott Kirkpatrick, C. Daniel Gelatt and Mario P. Vecchi at 1983.  Results: Concluding this work, is shown that is confronted a large real CB-CTT problem with 2507 courses conform by 1, 2, 3, 4, 5, 6, 7, or 8 lectures or hours, totalizing 9620 lectures; being able to assign into 316 rooms with different capacities, and also satisfying all the requested of 2178 professors and 1668 groups.  Discussion or Conclusion: In this research were developed various instances, where 3 of 5 are considered by the research community like large instances, and solved with SA Algorithm, regarding all feasible solutions. Thereby the metaheuristic methods are relatively good depending on the instance, and for this institution can offer a good solution.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen  Introducción: En ocasiones, los problemas de Optimización Combinatoria (COP), tal como el Problema de Calendarización de horarios (CTTP), se puede resolver utilizando técnicas Investigación Operativa (IO); sin embargo, cuando el problema aumenta de tamaño, la búsqueda de una solución se vuelve más compleja. Este tipo de problema es NP-duro por lo que requiere de procedimientos como métodos metaheurísticos con el fin de resolver el problema. Este trabajo aborda un Problema real en una Institución de Educación Superior Mexicana acerca de la Calendarización de horarios basada en la Curricula (CB-CTT). Cada institución tiene sus propias reglas operacionales, por lo tanto la modelación del problema es único ya que conserva sus propias características. Primero como parte de la aportación a la solución del problema, se elaboró un Software de Mediación (MS) con el fin de organizar los datos en bruto y eliminar la restricción dura en relación con los planes de estudio. Posteriormente, el problema se dividió en cinco instancias, de acuerdo a los cursos que comparten el mismo espacio físico, los cuales fueron resueltos mediante el algoritmo tradicional de Recocido Simulado (SA).El problema fue resuelto de manera satisfactoria, obteniendo la asignación de 9620 sesiones en 174.5 horas aproximadamente, aportando una solución sin particionar el problema en dos subproblemas, impactando positivamente la reducción del tiempo de elaboración de los horarios, proporcionando un horario factible y sin errores para toda la universidad.  Método: El Algoritmo de Recocido Simulado, cristalización simulada o enfriamiento simulado, es un algoritmo de búsqueda metaheurística para problemas de optimización; el objetivo general de este tipo de algoritmos es encontrar una buena aproximación al valor óptimo de una función en un espacio de búsqueda grande. A este valor se lo denomina "óptimo local u óptimo global". El nombre e inspiración viene del proceso de recocido del acero y cerámicas, una técnica que consiste en calentar y luego enfriar lentamente el material para variar sus propiedades físicas. El calor causa que los átomos aumenten su energía y que puedan así desplazarse de sus posiciones iniciales (un mínimo local de energía); el enfriamiento lento les da mayores probabilidades de recristalizar en configuraciones con menor energía que la inicial (mínimo global). El método fue descrito independientemente por Scott Kirkpatrick, C. Daniel Gelatt y Mario P. Vecchi en 1983.  Resultados: Como conclusión este trabajo muestra la solución a un problema real de gran tamaño CB-CTT con 2507 cursos conformados por 1, 2, 3, 4, 5, 6, 7 ó 8 sesiones u horas por curso, totalizando 9620 sesiones; se logró asignar en 316 habitaciones con diferentes capacidades, y también satisfacer cada una de las solicitudes de 2178 profesores y 1668 grupos.  Discusión o Conclusión: En esta investigación se han desarrollado diversas instancias, donde 3 de los 5 son considerados por la comunidad científica como instancias grandes, que se resuelve con el algoritmo SA, proporcionando todas las soluciones factibles. De esta manera se confirma que los métodos metaheurísticos son relativamente buenos dependiendo de las circunstancias y para esta institución puede ofrecer una buena solución.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Timetabling problem]]></kwd>
<kwd lng="en"><![CDATA[combinatorial optimization simulated annealing metaheuristic]]></kwd>
<kwd lng="en"><![CDATA[mediation software]]></kwd>
<kwd lng="es"><![CDATA[Problema de horarios]]></kwd>
<kwd lng="es"><![CDATA[optimización combinatoria]]></kwd>
<kwd lng="es"><![CDATA[metaheurística recocido simulado]]></kwd>
<kwd lng="es"><![CDATA[software de mediación]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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