Grouping Genetic Algorithm (GGA) for Electric Bus Fleet Scheduling

Abstract

The use of electric vehicles in the mobility of people is on the rise. This trend naturally is reflected in the public transport systems as well and presents a potential for clean transport in the cities with the reduction of operational costs for the providers of public transport systems. In this paper, we focus on one of the problems when introducing a fleet of electric buses to the city transport system, specifically the electric bus fleet scheduling problem. In this problem, an assignment of available electric buses to the service trips as well as the charging process and charging scheduling is addressed in order to minimize the number of used electric buses. The specifics of the electric buses such as the restricted driving range and long charging time need to be considered. The used type of charging is the opportunity charging, where the chargers are located at different places of the road network. To solve the problem, we propose a heuristic approach based on the grouping genetic algorithm, which utilizes the grouping character of the scheduling problem. The algorithm was tested on the datasets generated from the public transport system in the city of Žilina and compared to the results obtained by solving the previously presented mathematical model.