Planning the public transportation system of a city is a complex process that depends on various factors, including transportation modes, origin–destination demands, service quality and reliability, and operational costs. The vehicle frequency setting (FS) problem is a particularly challenging aspect of this planning process. This work proposes a novel methodology, based on biased random-key genetic algorithms (BRKGA), for optimizing the FS of a bus-based public transport system. The proposed approach considers two optimization models that aim to address the following key metrics: (i) passengers’ waiting time, and (ii) the operational cost for the concessionaire company, specifically the distance covered by buses. We apply our BRKGA methodology to a real case study using bus transport data from the city of Maceió (AL, Brazil). Our results demonstrate that, for each metric, the proposed methodology improves the performance of the city’s public transport system by over 10%, compared to the current configuration.
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