Otimização do sistema de transporte público utilizando algoritmo genético enviesado de chaves aleatórias

Abstract

The economy of a city or region is directly proportional to its public transport system efficiency. Planning of a public transport system depends on several factors such as transport modals, origin-destination demands, quality and reliability of this service, operational costs, among others. These features leads to very complex problems like the network design and vehicles frequency setting. The present work focus to propose a methodology, based on biased random-key genetic algorithms (BRKGA), for optmizing the vehicle frequency of the bus public transport system considering two metrics: (i) passengers waiting time; (ii) operational cost for the concessionaire company. The proposed methodology was applied in a real case study with bus transport data from the city of Maceió/AL. In this case, two different scenarios were considered: In the first scenario, it sought to minimize the passengers waiting time, and in the second, the objective was to minimize the operating cost of the concessionaire company. The results show that in both cases the proposed methodology was able to improve the performance of the configuration currently in use in the city’s public transport by over than 10%.