Examinando por Materia "heuristics"
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Ítem HouSI: Heuristic for delimitation of housing submarkets and price homogeneous areas(ELSEVIER SCI LTD, 2013-01-01) Royuela, V.; Duque, Juan C.; Universidad EAFIT. Departamento de Economía y Finanzas; Research in Spatial Economics (RISE)This paper seeks to address the problem of the empirical identification of housing market segmentation, once we assume that submarkets exist. The typical difficulty in identifying housing submarkets when dealing with many locations is the vast number of potential solutions and, in such cases, the use of the Chow test for hedonic functions is not a practical solution. Here, we solve this problem by undertaking an identification process with a heuristic for spatially constrained clustering, the "Housing Submarket Identifier" (HouSI). The solution is applied to the housing market in the city of Barcelona (Spain), where we estimate a hedonic model for fifty thousand dwellings aggregated into ten groups. In order to determine the utility of the procedure we seek to verify whether the final solution provided by the heuristic is comparable with the division of the city into ten administrative districts. © 2012 Elsevier Ltd.Ítem The max-p-regions problem(WILEY-BLACKWELL PUBLISHING INC, 2012-01-01) Duque, J.C.; Anselin, L.; Rey, S.J.; Universidad EAFIT. Departamento de Economía y Finanzas; Research in Spatial Economics (RISE)In this paper, we introduce a new spatially constrained clustering problem called the max-p-regions problem. It involves the clustering of a set of geographic areas into the maximum number of homogeneous regions such that the value of a spatially extensive regional attribute is above a predefined threshold value. We formulate the max-p-regions problem as a mixed integer programming (MIP) problem, and propose a heuristic solution. © 2011, Wiley Periodicals, Inc..Ítem The Network-Max-P-Regions model(TAYLOR & FRANCIS LTD, 2017-05-04) She, B.; Duque, J.C.; Ye, X.; Universidad EAFIT. Departamento de Economía y Finanzas; Research in Spatial Economics (RISE)This paper introduces a new p-regions model called the Network-Max-P-Regions (NMPR) model. The NMPR is a regionalization model that aims to aggregate n areas into the maximum number of regions (max-p) that satisfy a threshold constraint and to minimize the heterogeneity while taking into account the influence of a street network. The exact formulation of the NMPR is presented, and a heuristic solution is proposed to effectively compute the near-optimized partitions in several simulation datasets and a case study in Wuhan, China. © 2016 Informa UK Limited, trading as Taylor & Francis Group.Ítem Optimization of V-Trough photovoltaic concentrators through genetic algorithms with heuristics based on Weibull distributions(Elsevier Ltd, 2018-02-15) Arias-Rosales A.; Mejía-Gutiérrez R.; Universidad EAFIT. Departamento de Ingeniería de Diseño; Ingeniería de Diseño (GRID)Photovoltaic V-Troughs use simple and low-cost non-imaging optics, namely flat mirrors, to increase the solar harvesting area by concentrating the sunlight towards regular solar cells. The geometrical dispositions of the V-Trough's elements, and the way in which they are dynamically adjusted to track the sun, condition the optical performance. In order to improve their harvesting capacity, their geometrical set-up can be tailored to specific conditions and performance priorities. Given the large number of possible configurations and the interdependence of the multiple parameters involved, this work studies genetic algorithms as a heuristic approach for navigating the space of possible solutions. Among the algorithms studied, a new genetic algorithm named “GA-WA” (Genetic Algorithm-Weibull Arias) is proposed. GA-WA uses new heuristic processes based on Weibull distributions. Several V-Trough performance indicators are proposed as objective functions that can be optimized with genetic algorithms: (i) Ce? (average effective concentration); (ii) Cost (cost of materials) and (iii) Tsp (space required). Moreover, from the integration of these indicators, three multi-objective indices are proposed: (a) ICOE (Ce? versus Cost); (b) MICOE (Ce? versus Cost and Ce? versus Tsp combined) and (c) MDICOE (similar to MICOE but with discretization considerations). The heuristic parameters of the studied genetic algorithms are optimized and their capacities are explored in a case study. The results are compared against reported V-Trough set-ups designed with the interactive software VTDesign for the same case study. It was found that genetic algorithms, such as the ones developed in this work, are effective in the performance indicators improvement, as well as efficient and flexible tools in the problem of defining the set-up of solar V-Troughs in personalized scenarios. The intuition and the more holistic exploration of a trained engineer with an interactive software can be complemented with the broader and less biased evolutionary optimization of a tool like GA-WA. © 2017 Elsevier Ltd