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.

在这项工作中，我们解决了寻找最佳位置放置存放/收集共享自行车的站点的问题。为此，我们将问题建模为p中值问题，这是优化中主要存在的本地化问题。该p -median问题力求把一套设备（自行车站）在一组客户端之间的距离最小（公民）和自己最亲近的设施（自行车站）的方式。

我们使用遗传算法，迭代局部搜索，粒子群优化，模拟退火和变量邻域搜索来找到自行车站点的最佳位置并研究其比较优势。我们使用irace来自动参数化每个算法，以提供一种方法来自动微调算法。我们还研究了来自马拉加（西班牙）真实城市的各种开放数据源的不同真实数据（距离和权重），希望最终将其应用于智慧城市。我们已将我们的结果与马拉加已实施的解决方案进行了比较。最后，我们分析了如何使用提案通过增加站点来改善城市现有系统。