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Optimization vehicle Scheduling based on Simulated Annealing Genetic Algorithm

机译:基于模拟退火遗传算法的优化车辆调度

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It combines the own characteristics of vehicle scheduling, taking into account the both interests of the bus company and passengers, and establishes the vehicle scheduling plan model, taken the departure time (real value) as the gene variables to encode, and taken the difference between the adjacent grid spacing, the departure time interval of maximum and minimum, passenger load factor etc. to limit. With the example, the ~(GA-SA) model is improved to optimize the solution. Finally, it is possible to reliably find an optimal or near-optimal solution program on vehicle scheduling optimization problem in transit huge search space. Through comparative analysis of the experimental results, it can verify the effectiveness of the improved algorithm and superiority.
机译:它结合了车辆调度的自身特征,考虑到公交公司和乘客的两个利益,并建立了车辆调度计划模型,从出发时间(实际值)作为基因变量进行编码,并取得差异相邻的网格间距,最大和最小的出发时间间隔,乘客负荷因子等限制。使用该示例,改进了〜(GA-SA)模型以优化解决方案。最后,可以在运输巨大搜索空间中可靠地找到关于车辆调度优化问题的最佳或近最优的解决方案。通过对实验结果的比较分析,它可以验证改进算法和优越性的有效性。

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