Ficha T\u00e9cnica de los autores de los seminarios (2020 - 2021)<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\n\t\t\t\t\n\t\t\tLavinia Amorosi<\/h2><\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\n \tCV:\u00a0<\/strong>Lavinia Amorosi\u00a0is Researcher in Operations Research at the Department of Statistical Sciences of Sapienza, University of Rome. She received the Ph.D in Operational Research from Sapienza University of Rome in 2018.\u00a0Her research area is mainly on combinatorial optimization, with particular interest in network optimization and multi-objective programming, with application to real network problems in telecommunication, energy and transportation areas.<\/li>\n<\/ul>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\n \tAbstract: <\/strong>Complexity of problems deriving from real applications is leading to an increasing attention and adoption of multicriteria optimization in several sectors. One of these is the one of energy where new hybrid configurations also involving renewable energy sources targeting self-sufficiency are currently studied. In this talk I present a novel mathematical formulation to solve the problem of optimally sizing and managing battery energy storage for solar photovoltaic (PV) system integration of a multi-apartment building. The aim is the maximization of the collective self-consumption maintaining the control over time of the energy sold and bought and the monitoring of batteries state while it ranges from the minimum to the maximum levels. This is obtained through a new mathematical programming model with three different objective functions which can be simultaneously tuned to find problem Pareto optimal solutions. The computational results on detailed scenarios with real data verify the model adequacy to the real problem and give a measure of saving of bought and sold energy.<\/li>\n<\/ul>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\n\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t![\"\"](\"https:\/\/grupo.us.es\/fqm331\/wp-content\/uploads\/2020\/11\/Lavinia.png\")
\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\n\t\t\t\t\n\t\t\tMarina Leal<\/h2><\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\n \tCV: <\/strong>Marina studied Mathematics at the University of Alicante and obtained a Master in Statistics and Operations Research at the University of Murcia. She received her PhD in May 2019 from the University of Sevilla. She served as a postdoctoral researcher in the research training group \u201cAlgorithmic Optimization\u201d at Trier University. Currently, she is working as a Professor at the Miguel Hern\u00e1ndez University. Her main research interests comprise mixed-integer linear and nonlinear programming problems in different fields as bilevel optimization or robust optimization and diverse applications such as location and transportation theory, portfolio optimization, or energy problems.<\/li>\n<\/ul>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\n \tAbstract:\u00a0<\/strong>J.F. Benders introduced in 1962 a decomposition technique, currently known as Benders Decomposition, to solve problems with complicating variables. Since then, and especially in recent years, this technique has been extended and applied to a wide variety of optimization problems such as bilevel, robust, multiobjective, or stochastic problems, in many different applications such as location, transportation, finance, or energy. It allows to exploit the structure of the problem and to decentralize the computation, which results in the possibility of solving big problems. In this talk, we will go over the classical version of this Benders Decomposition technique, its generalizations, extensions, and improvements through concrete optimization applications.<\/li>\n<\/ul>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\n\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t![\"\"](\"https:\/\/grupo.us.es\/fqm331\/wp-content\/uploads\/2020\/11\/Marina.png\")
\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\n\t\t\t\t\n\t\t\tV\u00edctor Blanco<\/h2><\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\n \tCV:<\/strong>Graduate in Mathematics at the Universidad de Granada (UGR) and PhD in Operations Research at the Universidad de Sevilla. Since 2009, he is a researcher at the UGR. Currently, as associate professor at the IEMath-GR and the Dpt. of Quantitative Methods for Economics of Business. His research interests are in\u00a0different topics related to mathematical programming, both theoretical and applied, with special interests in the applications of optimization tools in (discrete and continuous) facility and hub location, data science and routing problems.<\/li>\n<\/ul>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\n \tAbstract:\u00a0<\/strong>In this talk we introduce a new version of the maximal covering location problem in a continuous framework,\u00a0in which the services to be located are required to be interconnected by means of a graph structure in\u00a0which two facilities are allowed to be linked if a given distance is not exceed.\u00a0These\u00a0\u2018linking requirements\u00bb are useful when finding the location of\u00a0telecommunication servers, that in case of being adequately connected, are able to provide a more accurate service by sharing capacity with other servers or providing\u00a0service to customers allocated to another facility in case of failure.\u00a0We provide\u00a0a general mathematical programming framework for the problem and different resolution strategies. We will derive some geometrical properties of the problem that allow us to project the continuous variables out avoiding the nonlinear constraints, resulting in an equivalent pure integer programming formulation.\u00a0Since the number of constraints in the integer programming formulation is large and the constraints are, in general, difficult to handle, we propose two branch-&-cut approaches that avoid the complete enumeration of the constraints resulting in more efficient procedures. We will show the results of our computational experience comparing the performance of the different approaches. This is a joint work with Ricardo Gazquez (PhD student at UGR).<\/li>\n<\/ul>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\n\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t![\"\"](\"https:\/\/grupo.us.es\/fqm331\/wp-content\/uploads\/2020\/11\/Victor.png\")
\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\n\t\t\t\t\n\t\t\tFrancisco Saldanha<\/h2><\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\n \tCV: <\/strong>Francisco Saldanha da Gama is professor of Operations Research in the Department of Statistics and Operations Research at the Faculty of Science, University of Lisbon, where he received his PhD in 2002. He has taught undergraduate and post-graduate courses focusing on Operations Research, Mathematical Programming, Discrete Optimization, Stochastic Optimization, and Logistics. His\u00a0research interests include stochastic mixed-integer optimization, location theory and project scheduling.\u200b<\/li>\n<\/ul>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\n \tAbstract:\u00a0<\/strong>In stochastic facility location it is often assumed that there is some CDF representing the underlying random vector, which is known beforehand. Unfortunately this may not be the case since such distribution may also be uncertain. This\u00a0seminar focuses on this aspect. In particular, the use of adaptative distributionally-robust optimization in the context of capacitated facility location problems is discussed. Different modeling aspects\u00a0and algorithmic methodologies\u00a0are covered.<\/li>\n<\/ul>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\n\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t![\"\"](\"https:\/\/grupo.us.es\/fqm331\/wp-content\/uploads\/2020\/11\/Francisco.png\")
\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\n\t\t\t\t\n\t\t\tJ.F. Monge<\/h2><\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\n \tCV:\u00a0<\/strong>Juan Francisco Monge es Licenciado en Matem\u00e1ticas por la Universidad de Valencia y Doctor en Estad\u00edstica e Investigaci\u00f3n Operativa por la Universidad Miguel Hern\u00e1ndez de Elche. Su principales areas de investigaci\u00f3n se han centrado en la optimizaci\u00f3n combinatoria y la optimizaci\u00f3n bajo incertidumbre, habiendo publicado m\u00e1s de 30 art\u00edculos de investigaci\u00f3n. Actualmente dirige el grupo de investigaci\u00f3n en Gesti\u00f3n de Recursos dentro del Intituto de Investigaci\u00f3n Operativa de la Universidad Miguel Hern\u00e1ndez de Elche.<\/li>\n<\/ul>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\n \tAbstract:\u00a0<\/strong>Dado un ranking de elementos, elementos que pertenecen a diferentes conjuntos, este ranking no implica generalmente un orden total entre los conjuntos. En este trabajo se presentar\u00e1 el problema de ordenamiento lineal de conjuntos y elementos basado en el problema de optimizaci\u00f3n combinatoria, Linear Ordering Problem (LOP). Se introducir\u00e1 el coeficiente de concordancia para medir el desorden de los elementos, permitiendo obtener el orden de conjuntos m\u00e1s cercano a un orden de elementos dado. Para la obtenci\u00f3n de la medida de concordancia se utilizar\u00e1 la distancia de Kendall, lo que permitir\u00e1 tambi\u00e9n presentar el coeficiente de concordancia como generaci\u00f3n al coeficiente de correlaci\u00f3n de Kendall, permitiendo as\u00ed medir la relaci\u00f3n existente en dos o m\u00e1s muestras. De esta forma, el coeficiente de concordancia permite definir una prueba no param\u00e9trica alternativa a la prueba Kruskal-Wallis. Se presentar\u00e1n algunos resultados te\u00f3ricos que permiten establecer la robustez del ordenamiento propuesto, as\u00ed como algunas aplicaciones: ordenaci\u00f3n de pa\u00edses a partir del orden de los colegios (informe PISA), ordenaci\u00f3n de tenistas a partir del orden de estos en las distintas estad\u00edsticas de juego, ordenaci\u00f3n de las caracter\u00edsticas m\u00e1s importantes para el juego de un tenista a partir del ranking ATP de jugadores.<\/li>\n<\/ul>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\n\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t![\"\"](\"https:\/\/grupo.us.es\/fqm331\/wp-content\/uploads\/2020\/11\/Monge.png\")
\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\n\t\t\t\t\t\t\t
Lavinia Amorosi<\/h2><\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\n \tCV:\u00a0<\/strong>Lavinia Amorosi\u00a0is Researcher in Operations Research at the Department of Statistical Sciences of Sapienza, University of Rome. She received the Ph.D in Operational Research from Sapienza University of Rome in 2018.\u00a0Her research area is mainly on combinatorial optimization, with particular interest in network optimization and multi-objective programming, with application to real network problems in telecommunication, energy and transportation areas.<\/li>\n<\/ul>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\n \tAbstract: <\/strong>Complexity of problems deriving from real applications is leading to an increasing attention and adoption of multicriteria optimization in several sectors. One of these is the one of energy where new hybrid configurations also involving renewable energy sources targeting self-sufficiency are currently studied. In this talk I present a novel mathematical formulation to solve the problem of optimally sizing and managing battery energy storage for solar photovoltaic (PV) system integration of a multi-apartment building. The aim is the maximization of the collective self-consumption maintaining the control over time of the energy sold and bought and the monitoring of batteries state while it ranges from the minimum to the maximum levels. This is obtained through a new mathematical programming model with three different objective functions which can be simultaneously tuned to find problem Pareto optimal solutions. The computational results on detailed scenarios with real data verify the model adequacy to the real problem and give a measure of saving of bought and sold energy.<\/li>\n<\/ul>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\n\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\n\t\t\t\t\n\t\t\tMarina Leal<\/h2><\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\n \tCV: <\/strong>Marina studied Mathematics at the University of Alicante and obtained a Master in Statistics and Operations Research at the University of Murcia. She received her PhD in May 2019 from the University of Sevilla. She served as a postdoctoral researcher in the research training group \u201cAlgorithmic Optimization\u201d at Trier University. Currently, she is working as a Professor at the Miguel Hern\u00e1ndez University. Her main research interests comprise mixed-integer linear and nonlinear programming problems in different fields as bilevel optimization or robust optimization and diverse applications such as location and transportation theory, portfolio optimization, or energy problems.<\/li>\n<\/ul>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\n \tAbstract:\u00a0<\/strong>J.F. Benders introduced in 1962 a decomposition technique, currently known as Benders Decomposition, to solve problems with complicating variables. Since then, and especially in recent years, this technique has been extended and applied to a wide variety of optimization problems such as bilevel, robust, multiobjective, or stochastic problems, in many different applications such as location, transportation, finance, or energy. It allows to exploit the structure of the problem and to decentralize the computation, which results in the possibility of solving big problems. In this talk, we will go over the classical version of this Benders Decomposition technique, its generalizations, extensions, and improvements through concrete optimization applications.<\/li>\n<\/ul>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\n\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\n\t\t\t\t\n\t\t\tV\u00edctor Blanco<\/h2><\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\n \tCV:<\/strong>Graduate in Mathematics at the Universidad de Granada (UGR) and PhD in Operations Research at the Universidad de Sevilla. Since 2009, he is a researcher at the UGR. Currently, as associate professor at the IEMath-GR and the Dpt. of Quantitative Methods for Economics of Business. His research interests are in\u00a0different topics related to mathematical programming, both theoretical and applied, with special interests in the applications of optimization tools in (discrete and continuous) facility and hub location, data science and routing problems.<\/li>\n<\/ul>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\n \tAbstract:\u00a0<\/strong>In this talk we introduce a new version of the maximal covering location problem in a continuous framework,\u00a0in which the services to be located are required to be interconnected by means of a graph structure in\u00a0which two facilities are allowed to be linked if a given distance is not exceed.\u00a0These\u00a0\u2018linking requirements\u00bb are useful when finding the location of\u00a0telecommunication servers, that in case of being adequately connected, are able to provide a more accurate service by sharing capacity with other servers or providing\u00a0service to customers allocated to another facility in case of failure.\u00a0We provide\u00a0a general mathematical programming framework for the problem and different resolution strategies. We will derive some geometrical properties of the problem that allow us to project the continuous variables out avoiding the nonlinear constraints, resulting in an equivalent pure integer programming formulation.\u00a0Since the number of constraints in the integer programming formulation is large and the constraints are, in general, difficult to handle, we propose two branch-&-cut approaches that avoid the complete enumeration of the constraints resulting in more efficient procedures. We will show the results of our computational experience comparing the performance of the different approaches. This is a joint work with Ricardo Gazquez (PhD student at UGR).<\/li>\n<\/ul>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\n\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\n\t\t\t\t\n\t\t\tFrancisco Saldanha<\/h2><\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\n \tCV: <\/strong>Francisco Saldanha da Gama is professor of Operations Research in the Department of Statistics and Operations Research at the Faculty of Science, University of Lisbon, where he received his PhD in 2002. He has taught undergraduate and post-graduate courses focusing on Operations Research, Mathematical Programming, Discrete Optimization, Stochastic Optimization, and Logistics. His\u00a0research interests include stochastic mixed-integer optimization, location theory and project scheduling.\u200b<\/li>\n<\/ul>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\n \tAbstract:\u00a0<\/strong>In stochastic facility location it is often assumed that there is some CDF representing the underlying random vector, which is known beforehand. Unfortunately this may not be the case since such distribution may also be uncertain. This\u00a0seminar focuses on this aspect. In particular, the use of adaptative distributionally-robust optimization in the context of capacitated facility location problems is discussed. Different modeling aspects\u00a0and algorithmic methodologies\u00a0are covered.<\/li>\n<\/ul>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\n\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\n\t\t\t\t\n\t\t\tJ.F. Monge<\/h2><\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\n \tCV:\u00a0<\/strong>Juan Francisco Monge es Licenciado en Matem\u00e1ticas por la Universidad de Valencia y Doctor en Estad\u00edstica e Investigaci\u00f3n Operativa por la Universidad Miguel Hern\u00e1ndez de Elche. Su principales areas de investigaci\u00f3n se han centrado en la optimizaci\u00f3n combinatoria y la optimizaci\u00f3n bajo incertidumbre, habiendo publicado m\u00e1s de 30 art\u00edculos de investigaci\u00f3n. Actualmente dirige el grupo de investigaci\u00f3n en Gesti\u00f3n de Recursos dentro del Intituto de Investigaci\u00f3n Operativa de la Universidad Miguel Hern\u00e1ndez de Elche.<\/li>\n<\/ul>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\n \tAbstract:\u00a0<\/strong>Dado un ranking de elementos, elementos que pertenecen a diferentes conjuntos, este ranking no implica generalmente un orden total entre los conjuntos. En este trabajo se presentar\u00e1 el problema de ordenamiento lineal de conjuntos y elementos basado en el problema de optimizaci\u00f3n combinatoria, Linear Ordering Problem (LOP). Se introducir\u00e1 el coeficiente de concordancia para medir el desorden de los elementos, permitiendo obtener el orden de conjuntos m\u00e1s cercano a un orden de elementos dado. Para la obtenci\u00f3n de la medida de concordancia se utilizar\u00e1 la distancia de Kendall, lo que permitir\u00e1 tambi\u00e9n presentar el coeficiente de concordancia como generaci\u00f3n al coeficiente de correlaci\u00f3n de Kendall, permitiendo as\u00ed medir la relaci\u00f3n existente en dos o m\u00e1s muestras. De esta forma, el coeficiente de concordancia permite definir una prueba no param\u00e9trica alternativa a la prueba Kruskal-Wallis. Se presentar\u00e1n algunos resultados te\u00f3ricos que permiten establecer la robustez del ordenamiento propuesto, as\u00ed como algunas aplicaciones: ordenaci\u00f3n de pa\u00edses a partir del orden de los colegios (informe PISA), ordenaci\u00f3n de tenistas a partir del orden de estos en las distintas estad\u00edsticas de juego, ordenaci\u00f3n de las caracter\u00edsticas m\u00e1s importantes para el juego de un tenista a partir del ranking ATP de jugadores.<\/li>\n<\/ul>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\n\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\n\t\t\t\t\t\t\t
- \n \t
CV:\u00a0<\/strong>Lavinia Amorosi\u00a0is Researcher in Operations Research at the Department of Statistical Sciences of Sapienza, University of Rome. She received the Ph.D in Operational Research from Sapienza University of Rome in 2018.\u00a0Her research area is mainly on combinatorial optimization, with particular interest in network optimization and multi-objective programming, with application to real network problems in telecommunication, energy and transportation areas.<\/li>\n<\/ul>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t Abstract: <\/strong>Complexity of problems deriving from real applications is leading to an increasing attention and adoption of multicriteria optimization in several sectors. One of these is the one of energy where new hybrid configurations also involving renewable energy sources targeting self-sufficiency are currently studied. In this talk I present a novel mathematical formulation to solve the problem of optimally sizing and managing battery energy storage for solar photovoltaic (PV) system integration of a multi-apartment building. The aim is the maximization of the collective self-consumption maintaining the control over time of the energy sold and bought and the monitoring of batteries state while it ranges from the minimum to the maximum levels. This is obtained through a new mathematical programming model with three different objective functions which can be simultaneously tuned to find problem Pareto optimal solutions. The computational results on detailed scenarios with real data verify the model adequacy to the real problem and give a measure of saving of bought and sold energy.<\/li>\n<\/ul>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t CV: <\/strong>Marina studied Mathematics at the University of Alicante and obtained a Master in Statistics and Operations Research at the University of Murcia. She received her PhD in May 2019 from the University of Sevilla. She served as a postdoctoral researcher in the research training group \u201cAlgorithmic Optimization\u201d at Trier University. Currently, she is working as a Professor at the Miguel Hern\u00e1ndez University. Her main research interests comprise mixed-integer linear and nonlinear programming problems in different fields as bilevel optimization or robust optimization and diverse applications such as location and transportation theory, portfolio optimization, or energy problems.<\/li>\n<\/ul>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t Abstract:\u00a0<\/strong>J.F. Benders introduced in 1962 a decomposition technique, currently known as Benders Decomposition, to solve problems with complicating variables. Since then, and especially in recent years, this technique has been extended and applied to a wide variety of optimization problems such as bilevel, robust, multiobjective, or stochastic problems, in many different applications such as location, transportation, finance, or energy. It allows to exploit the structure of the problem and to decentralize the computation, which results in the possibility of solving big problems. In this talk, we will go over the classical version of this Benders Decomposition technique, its generalizations, extensions, and improvements through concrete optimization applications.<\/li>\n<\/ul>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t CV:<\/strong>Graduate in Mathematics at the Universidad de Granada (UGR) and PhD in Operations Research at the Universidad de Sevilla. Since 2009, he is a researcher at the UGR. Currently, as associate professor at the IEMath-GR and the Dpt. of Quantitative Methods for Economics of Business. His research interests are in\u00a0different topics related to mathematical programming, both theoretical and applied, with special interests in the applications of optimization tools in (discrete and continuous) facility and hub location, data science and routing problems.<\/li>\n<\/ul>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t Abstract:\u00a0<\/strong>In this talk we introduce a new version of the maximal covering location problem in a continuous framework,\u00a0in which the services to be located are required to be interconnected by means of a graph structure in\u00a0which two facilities are allowed to be linked if a given distance is not exceed.\u00a0These\u00a0\u2018linking requirements\u00bb are useful when finding the location of\u00a0telecommunication servers, that in case of being adequately connected, are able to provide a more accurate service by sharing capacity with other servers or providing\u00a0service to customers allocated to another facility in case of failure.\u00a0We provide\u00a0a general mathematical programming framework for the problem and different resolution strategies. We will derive some geometrical properties of the problem that allow us to project the continuous variables out avoiding the nonlinear constraints, resulting in an equivalent pure integer programming formulation.\u00a0Since the number of constraints in the integer programming formulation is large and the constraints are, in general, difficult to handle, we propose two branch-&-cut approaches that avoid the complete enumeration of the constraints resulting in more efficient procedures. We will show the results of our computational experience comparing the performance of the different approaches. This is a joint work with Ricardo Gazquez (PhD student at UGR).<\/li>\n<\/ul>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t CV: <\/strong>Francisco Saldanha da Gama is professor of Operations Research in the Department of Statistics and Operations Research at the Faculty of Science, University of Lisbon, where he received his PhD in 2002. He has taught undergraduate and post-graduate courses focusing on Operations Research, Mathematical Programming, Discrete Optimization, Stochastic Optimization, and Logistics. His\u00a0research interests include stochastic mixed-integer optimization, location theory and project scheduling.\u200b<\/li>\n<\/ul>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t Abstract:\u00a0<\/strong>In stochastic facility location it is often assumed that there is some CDF representing the underlying random vector, which is known beforehand. Unfortunately this may not be the case since such distribution may also be uncertain. This\u00a0seminar focuses on this aspect. In particular, the use of adaptative distributionally-robust optimization in the context of capacitated facility location problems is discussed. Different modeling aspects\u00a0and algorithmic methodologies\u00a0are covered.<\/li>\n<\/ul>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t CV:\u00a0<\/strong>Juan Francisco Monge es Licenciado en Matem\u00e1ticas por la Universidad de Valencia y Doctor en Estad\u00edstica e Investigaci\u00f3n Operativa por la Universidad Miguel Hern\u00e1ndez de Elche. Su principales areas de investigaci\u00f3n se han centrado en la optimizaci\u00f3n combinatoria y la optimizaci\u00f3n bajo incertidumbre, habiendo publicado m\u00e1s de 30 art\u00edculos de investigaci\u00f3n. Actualmente dirige el grupo de investigaci\u00f3n en Gesti\u00f3n de Recursos dentro del Intituto de Investigaci\u00f3n Operativa de la Universidad Miguel Hern\u00e1ndez de Elche.<\/li>\n<\/ul>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t Abstract:\u00a0<\/strong>Dado un ranking de elementos, elementos que pertenecen a diferentes conjuntos, este ranking no implica generalmente un orden total entre los conjuntos. En este trabajo se presentar\u00e1 el problema de ordenamiento lineal de conjuntos y elementos basado en el problema de optimizaci\u00f3n combinatoria, Linear Ordering Problem (LOP). Se introducir\u00e1 el coeficiente de concordancia para medir el desorden de los elementos, permitiendo obtener el orden de conjuntos m\u00e1s cercano a un orden de elementos dado. Para la obtenci\u00f3n de la medida de concordancia se utilizar\u00e1 la distancia de Kendall, lo que permitir\u00e1 tambi\u00e9n presentar el coeficiente de concordancia como generaci\u00f3n al coeficiente de correlaci\u00f3n de Kendall, permitiendo as\u00ed medir la relaci\u00f3n existente en dos o m\u00e1s muestras. De esta forma, el coeficiente de concordancia permite definir una prueba no param\u00e9trica alternativa a la prueba Kruskal-Wallis. Se presentar\u00e1n algunos resultados te\u00f3ricos que permiten establecer la robustez del ordenamiento propuesto, as\u00ed como algunas aplicaciones: ordenaci\u00f3n de pa\u00edses a partir del orden de los colegios (informe PISA), ordenaci\u00f3n de tenistas a partir del orden de estos en las distintas estad\u00edsticas de juego, ordenaci\u00f3n de las caracter\u00edsticas m\u00e1s importantes para el juego de un tenista a partir del ranking ATP de jugadores.<\/li>\n<\/ul>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\n \t
\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\tMarina Leal<\/h2><\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
\n \t
\n \t
\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\tV\u00edctor Blanco<\/h2><\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
\n \t
\n \t
\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\tFrancisco Saldanha<\/h2><\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
\n \t
\n \t
\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\tJ.F. Monge<\/h2><\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
\n \t
\n \t
\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t