Intelligent Scheduling of Public Traffic Vehicles Based on a Hybrid Genetic Algorithm

A genetic algorithm (GA) and a hybrid genetic algorithm (HGA) were used for optimal scheduling of public vehicles based on their actual operational environments. The performance for three kinds of ve-hicular levels were compared using one-point and two-point crossover operations. The vehicle scheduling times are improved by the intelligent characteristics of the GA. The HGA, which integrates the genetic algo-rithm with a tabu search, further improves the convergence performance and the optimization by avoiding the premature convergence of the GA. The results show that intelligent scheduling of public vehicles based on the HGA overcomes the shortcomings of traditional scheduling methods. The vehicle operation man-agement efficiency is improved by this essential technology for intelligent scheduling of public vehicles.