Using metaheuristics for the location of bicycle stations
- URL: http://arxiv.org/abs/2402.03945v1
- Date: Tue, 6 Feb 2024 12:19:46 GMT
- Title: Using metaheuristics for the location of bicycle stations
- Authors: Christian Cintrano, Francisco Chicano, Enrique Alba
- Abstract summary: 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)
- Score: 4.2847927405489195
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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