Study on transit scheduling optimization based on improved genetic-simulated annealing algorithm
基于改进遗传—模拟退火算法的公交排班优化研究

In combination of the characteristic of public traffic vehicles' scheduling, established the optimization model of public transportation vehicles' scheduling, giving attention to the benefits of passengers and companies. Adopting the coding method using departing time as gene variable, this paper proposed the improved genetic-simulated annealing algorithm by imposing the constraints on the time difference between the two bus headways, the maximum and the minimum of the bus headway, and passenger load rate. It adopted the algorithm to find solution of the model which overcame the advantages of traditional optimization algorithms, improved the solving efficiency. Finally, it obtained the simulation results by using the improved genetic-simulated annealing algorithm for solving the non-uniform grid scheduling. Results show that the improved genetic-simulated annealing algorithm can find the approximate best result in the huge search space of optimization, while greatly increases the computational efficiency.