Genetic Algorithm-Based Approach for Optimal Location of Transit Repair Vehicles on a Large Urban Network

Abstract

Determining the optimal location of a fleet of vehicles is necessary in a number of potential applications, such as special repair vehicles for buses on a large public transportation network. The Athens Urban Transport Authority operates a large bus fleet over an extensive network for 19 h a day and serves a population of approximately 4 million people, all in a heavily congested road network. During the 2004 Summer Olympic Games, held in Athens, most spectators, employees, and volunteers were transported to and from Olympic Games venues by public transportation. Dedicated Olympic Games bus lines operated under a tight around-the-clock schedule. During normal operations and particularly during events such as the Olympic Games, incidents such as vehicle breakdowns and minor accidents can have a severe effect on the operation of the public transport network and can cause a significant decrease in the level of service. To help the authority locate bus repair vehicles over the entire network, a decision support system was developed on the basis of an embedded genetic algorithm used for obtaining optimal location solutions. The system's design and performance make it easy to operate under real-time conditions, which is useful for planning and for fast vehicle redeployment.