摘要
建立了成对与不成对列车运行图的机车周转图的数学模型和相应的机车最优配置的遗传算法.用单段映射交叉和基于知识的变异方法以及交叉概率,变异概率随个体优劣程度自适应调整策略,提高了局部搜索能力以及收敛和优化性能.以某区段实际运行图为例,用本文方法使机车总消耗时间和需要的机车数分别减少约5.7%和7.7%;用文献中的实例数据计算,与原方法相比,减少了机车总消耗时间.
A mathematical model for a locomotive diagram of a train diagram with paired and nonpaired trains was presented, and the optimized schedule was obtained with a genetic algorithm. The abilities of local search, convergence and optimization were raised with a two-point crossover operator and a knowledge-based mutation operator. The proposed method was tested over an actual problem of train diagram for a district on a railway line. The results show that the total time of locomotive operation and the required number of locomotives are reduced by about 5.7% and 7.7% , respectively. Another result shows that the proposed method reduces total time of locomotive operation compared with the method presented and for the same data taken in the same paper.
出处
《西南交通大学学报》
EI
CSCD
北大核心
2006年第3期273-278,共6页
Journal of Southwest Jiaotong University
关键词
机车周转图
遗传算法
自适应
优化
铁路
locomotive diagram
genetic algorithm
self-adaptive
optimization
railway
