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

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

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.

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

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

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