10 research outputs found

    Studying Solutions of the p-Median Problem for the Location of Public Bike Stations

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    The use of bicycles as a means of transport is becoming more and more popular today, especially in urban areas, to avoid the disadvantages of individual car traffic. In fact, city managers react to this trend and actively promote the use of bicycles by providing a network of bicycles for public use and stations where they can be stored. Establishing such a network involves the task of finding best locations for stations, which is, however, not a trivial task. In this work, we examine models to determine the best location of bike stations so that citizens will travel the shortest distance possible to one of them. Based on real data from the city of Malaga, we formulate our problem as a p-median problem and solve it with a variable neighborhood search algorithm that was automatically configured with irace. We compare the locations proposed by the algorithm with the real ones used currently by the city council. We also study where new locations should be placed if the network grows.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. This research was partially funded by the University of Málaga, Andalucı́a Tech, the Spanish MINECO and FEDER projects: TIN2014- 57341-R, TIN2016-81766-REDT, and TIN2017-88213-R. C. Cintrano is supported by a FPI grant (BES-2015-074805) from Spanish MINECO

    Recommender system based on concept inference

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    Este Trabajo Fin de Grado (TFG) tiene como objetivo la creación de un framework para su uso en sistemas de recomendación. Este TFG se ha realizado por dos personas en la modalidad de trabajo en equipo. El trabajo se dividió en dos partes, una realizada conjuntamente y la otra de manera individual. La parte conjunta tiene como objetivo construir un sistema que sea capaz de, a partir de comentarios y opiniones sobre puntos de interés (POIs) y haciendo uso de la herramienta de procesamiento de lenguaje natural AlchemyAPI, construir contextos formales. Éste es el eje principal de la teoría del análisis formal de conceptos (FCA) propuesta por Bernhard Ganter. Además será el punto de partida de la segunda parte (individual), que consistirá en aplicar otra parte de la teoría de FCA para obtener, a partir del contexto, el retículo de conceptos mediante la implementación en Java del algoritmo Titanic. Estos conceptos podrán ser usados para obtener grupos de usuarios y tendencias. El sistema se ha implementado como una aplicación web Java EE versión 6 y una API para trabajar con contextos formales. Para el desarrollo web se han empleado tecnologías actuales como Spring y jQuery. Este proyecto se presenta como un trabajo inicial en el que se expondrán, además del sistema construido, diversos problemas relacionados con los sistemas de recomendación y se propondrán líneas para futuros TFGs

    Multiobjective electric vehicle charging station locations in a city scale area: Malaga study case.

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    This article presents a multiobjective variation of the problem of locating electric vehicle charging stations (EVCS) in a city known as the Multiobjective Electric Vehicle Charging Stations Locations (MO-EVCS-L) problem. MO-EVCS-L considers two conflicting objectives: maximizing the quality of service of the charging station network and minimizing the deployment cost when installing different types of charging stations. Two multiobjective metaheuristics are proposed to address MO-EVCS-L: the Non-dominated Sorting Genetic Algorithm, version II (NSGA-II) and the Strength Pareto Evolutionary Algorithm, version 2 (SPEA2). The experimental analysis is performed on a real-world case study defined in Malaga, Spain, and it compares the proposed approaches with a baseline algorithm. Results show that the SPEA2 computes the most competitive solutions, even though both metaheuristics found an accurate set of solutions that provide different trade-offs between the quality of service and the installation costs.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

    Using metaheuristics for the location of bicycle stations

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    In this work, we solve the problem of finding the best locations to place stations for depositing/collecting shared bicycles. To do this, we model the problem as the p-median problem, that is a major existing localization problem in optimization. The p-median problem seeks to place a set of facilities (bicycle stations) in a way that minimizes the distance between a set of clients (citizens) and their closest facility (bike station). We have used a genetic algorithm, iterated local search, particle swarm optimization, simulated annealing, and variable neighbourhood search, to find the best locations for the bicycle stations and study their comparative advantages. We use irace to parameterize each algorithm automatically, to contribute with a methodology to fine-tune algorithms automatically. We have also studied different real data (distance and weights) from diverse open data sources from a real city, Malaga (Spain), hopefully leading to a final smart city application. We have compared our results with the implemented solution in Malaga. Finally, we have analyzed how we can use our proposal to improve the existing system in the city by adding more stations.This research was partially funded by the University of Málaga, Andalucía Tech, the Spanish MINECO and FEDER projects: TIN2014-57341-R, TIN2016-81766-REDT, and TIN2017-88213-R. C. Cintrano is supported by a FPI grant (BES-2015-074805) from Spanish MINECO

    Facing robustness as a multi-objective problem: A bi-objective shortest path problem in smart regions

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    The goal in Robust Optimization is to optimize not only the quality of the solutions but also the variation of this quality with the uncertain parameters of the optimization problem. We propose a robust model for the bi-objective shortest path problem applied in a smart mobility context: Finding routes for cars in a city to minimize travel time and gas emissions. Our proposal treats robustness from a multi-objective point of view. We model the parameters that define each instance as random variables, described through their mean and variance. In this way, we can obtain efficient solutions that are also less sensitive to changes in the environment. We run different types of algorithms in multiple instances to solve this problem so that we obtain a global view of the behavior of different techniques. All experimentation uses a scenario based on real data: The province of Malaga, Spain. This realistic settlement for our study allows us to test the applicability of our model in final systems for the citizens. The results clearly state the interest of our proposal for tackling robustness and represents a new state-of-the-art in smart mobility, an always appealing feature of works, that could lead to an industrial prototype.This research has been partially funded by the Spanish MINECO and FEDER projects TIN2014-57341-R, TIN2016-81766-REDT, and TIN2017-88213-R. C. Cintrano is supported by a FPI grant (BES-2015-074805) from Spanish MINECO. It has also been partially funded bythe Universidad de Málaga, Andalucia TECH

    Tile map size optimization for real world routing by using differential evolution

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    Finding the shortest path between two places is a well known problem in road traveling. While most of the work done up to this moment is focused on algorithmics, efficiently managing the information has received significantly less attention. Nevertheless, real world problems like road map routing present a challenge due to the impact that the immense size of the map has over the temporal complexity of the routing algorithms. In this work we propose a strategy for efficiently computing the shortest path in real road maps based on data managing: the tile map partitioning. To recreate a real scenario, we implemented a routing system and we tested our strategy using the road map of the Province of Málaga, Spain. Using a Differential Evolution we found the optimal tile size and prove that significant time reductions can be achieved by using the tile map partitioning.The Mexican author wishes to express his gratitude to “CONACyT, Consejo Nacional de Ciencia y Tecnología de México” for its economical support in the program “Estancias Posdoctorales Internacionales 2015-2016” (project number 263564), and to Cesar Bonavides-Martinez, from the Center for Genomic Sciences, UNAM, for his technical support. This research was partially funded by the University of Málaga, Andalucía Tech, the Spanish Ministry of Economy and Competitiveness, and FEDER (grants TIN2014-57341-R and 8.06/5.47.4142)

    Hybridization of Evolutionary Operators with Elitist Iterated Racing for the Simulation Optimization of Traffic Lights Programs.

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    In the traffic light scheduling problem, the evaluation of candidate solutions requires the simulation of a process under various (traffic) scenarios. Thus, good solutions should not only achieve good objective function values, but they must be robust (low variance) across all different scenarios. Previous work has shown that combining IRACE with evolutionary operators is effective for this task due to the power of evolutionary operators in numerical optimization. In this article, we further explore the hybridization of evolutionary operators and the elitist iterated racing of IRACE for the simulation–optimization of traffic light programs. We review previous works from the literature to find the evolutionary operators performing the best when facing this problem to propose new hybrid algorithms. We evaluate our approach over a realistic case study derived from the traffic network of Málaga (Spain) with 275 traffic lights that should be scheduled optimally. The experimental analysis reveals that the hybrid algorithm comprising IRACE plus differential evolution offers statistically better results than the other algorithms when the budget of simulations is low. In contrast, IRACE performs better than the hybrids for a high simulations budget, although the optimization time is much longer.This research was partially funded by the University of Malaga, Andaluc ´ ´ıa Tech and the project TAILOR Grant #952215, H2020-ICT-2019-3. C. Cintrano is supported by a FPI grant (BES-2015-074805) from Spanish MINECO. M. Lopez-Ib ´ a´nez is a ˜ “Beatriz Galindo” Senior Distinguished Researcher (BEAGAL 18/00053) funded by the Ministry of Science and Innovation of the Spanish Government. J. Ferrer is supported by a postdoc grant (DOC/00488) funded by the Andalusian Ministry of Economic Transformation, Industry, Knowledge and Universities

    Realistic Modeling of Smart Mobility Problems with Environmental Concerns

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    Esta tesis da los primeros pasos en la mejora medioambiental y la optimización de la movilidad. Sin embargo, no nos detendremos aquí, y seguiremos trabajando en iniciativas sostenibles para mejorar las ciudades, ayudar a la vida cotidiana de los ciudadanos y mejorar el medio ambiente.El tráfico en las ciudades se ha convertido en un problema de vital importancia. No solo por los problemas logísticos, sino también por las emisiones de gases asociadas. Considerar el impacto que la movilidad tiene sobre el medioambiente es vital para un futuro sostenible. El objetivo número 11 “Ciudades y Comunidades Sostenibles”, de los Objetivos de Desarrollo Sostenible, promueve las Ciudades Inteligentes desde la perspectiva de la sostenibilidad. En esta tesis, queremos sumergirnos en el problema de la mejora medioambiental y el transporte ecológico. Sin embargo, muchos trabajos en la literatura no tienen en cuenta estos objetivos o utilizan escenarios sintéticos para probar sus aportaciones. Nosotros nos basamos en datos abiertos, que incluimos en nuestros modelos para obtener resultados realistas. Además, centramos nuestro estudio en la ciudad real de Málaga, España. Siguiendo esta línea, también hemos ofrecido todos los resultados, códigos e instancias como datos abiertos de investigación. Hemos empleado diferentes algoritmos de optimización para trabajar con los problemas presentados en esta tesis. Nos hemos centrado principalmente, aunque no exclusivamente, en los algoritmos metaheurísticos, que han demostrado su eficacia en muchos otros problemas del mundo real. En esta tesis se han trabajado con varios problemas de movilidad ecológica. Esta investigación se ha plasmado en diferentes contribuciones que avalan esta tesis: 3 artículos en revistas JCR (todos Q1) y 14 artículos en congresos. Hemos destacado varios problemas de la movilidad actual en las ciudades. Los problemas seleccionados son la planificación óptima de los semáforos, el análisis del tráfico considerando los vehículos de carga, la colocación inteligente de las infraestructuras necesarias para el transporte ecológico (bicicletas y vehículos eléctricos) y el problema del encaminamiento de vehículos considerando diversos aspectos como el tiempo de viaje y las emisiones

    Studying Solutions of the p-Median Problem for the Location of Public Bike Stations

    No full text
    The use of bicycles as a means of transport is becoming more and more popular today, especially in urban areas, to avoid the disadvantages of individual car traffic. In fact, city managers react to this trend and actively promote the use of bicycles by providing a network of bicycles for public use and stations where they can be stored. Establishing such a network involves the task of finding best locations for stations, which is, however, not a trivial task. In this work, we examine models to determine the best location of bike stations so that citizens will travel the shortest distance possible to one of them. Based on real data from the city of Malaga, we formulate our problem as a p-median problem and solve it with a variable neighborhood search algorithm that was automatically configured with irace. We compare the locations proposed by the algorithm with the real ones used currently by the city council. We also study where new locations should be placed if the network grows.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. This research was partially funded by the University of Málaga, Andalucı́a Tech, the Spanish MINECO and FEDER projects: TIN2014- 57341-R, TIN2016-81766-REDT, and TIN2017-88213-R. C. Cintrano is supported by a FPI grant (BES-2015-074805) from Spanish MINECO
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