基于改进遗传—模拟退火算法的公交排班优化研究

结合公交车辆调度自身的特点,兼顾公交公司与乘客双方的利益,建立了公交排班优化模型,以发车时刻为基因变量进行编码,对两个相邻的发车间隔之差、最大最小发车时间间隔、乘客的满载率等条件进行约束限制,提出了基于改进的遗传—模拟退火算法;对该模型进行优化求解,克服了传统优化算法的缺陷,提高了优化设计过程的求解效率。通过仿真实验得到了利用改进的遗传—模拟退火算法进行求解的不均匀发车时刻表。结果表明,改进的遗传—模拟退火算法能够在公交智能排班优化问题的巨大搜索空间中可靠地找到近似最优解,大大提高了计算效率。

Study on transit scheduling optimization based on improved genetic-simulated annealing algorithm

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.