2026-06-01T09:43:57Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/297742026-02-03T11:05:29Zcom_10630_2254col_10630_37953

00925njm 22002777a 4500 dc Cintrano López, Christian author Chicano-García, José-Francisco author Alba-Torres, Enrique author 2020 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. Christian Cintrano, Francisco Chicano, Enrique Alba: Using metaheuristics for the location of bicycle stations. Expert Syst. Appl. 161: 113684 (2020) https://hdl.handle.net/10630/29774 doi.org/10.1016/j.eswa.2020.113684 Optimización matemática Using metaheuristics for the location of bicycle stations