不固定牵引方式双肩回交路机车最优配置的遗传算法

机车周转图的编制是多约束条件的组合优化问题。针对不固定牵引方式双肩回交路机车周转图编制建立数学模型,并以机车在2个折返段(站)和基本段(站)总停留时间最少为目标,设计不固定牵引方式双肩回交路机车运转制下求解机车最优配置的遗传算法。基于知识的变异方法和采用交叉概率、变异概率随个体优劣程度自适应的调整策略,提高算法的局部搜索能力以及收敛和优化性能。以某实际列车运行图为例进行仿真计算,结果表明:运用该算法可使机车的段内总停留时间和需要的牵引机车台数较实际的机车周转图分别减少约21%和8.6%。

A Genetic Algorithm for Obtaining the Optimal Locomotive Working Diagram with Double-Shoulder Circuit Locomotive Routing by the Mode of Unfixed Traction

Locomotive working diagram is a multi-constraint combinatorial optimization problem.A mathematical model is established for double-shoulder circuit locomotive routing by the mode of unfixed traction.The objective is to minimize the total time for locomotives staying in districts,and the optimized schedule is obtained with a genetic algorithm.The abilities of local search,convergence and optimization are raised with the knowledge-based mutation operator and the crossing probability,mutating probability self-adjusted by the fitness of the individual.The proposed method was tested over an actual problem of train working diagram.The results show that compared with actual locomotive working diagram,the total time of locomotives staying in districts and the required number of locomotives is reduced by about 21% and 8.6% respectively.