基于改进遗传算法的车辆调度模型  

Vehicle scheduling model based on improved Genetic Algorithm

作　　者：曾羽琚[1] 陈明辉[2] 

机构地区：[1]长沙环境保护职业技术学院信息技术系,长沙410007 [2]湖南商学院教务处,长沙410205

出　　处：《计算机工程与应用》2015年第6期240-243,共4页Computer Engineering and Applications

基　　金：湖南省普通高等学校科学研究项目(No.14C0016);长沙环境保护职业技术学院基金(No.12JY009);中国职业技术教育学会;湖南省职业教育与成人教育学会;高职环境类专业学生数据处理能力培养的研究(No.XHB2013015)

摘　　要：随着运输网络复杂程度的不断增加,运输车辆会遇到车祸、拥堵等干扰,传统的车辆调度模型缺少对这种干扰风险的分析,无法建立较为准确的调度模型,造成调度车辆遇到干扰时,调度效率大幅降低。为了避免上述缺陷,提出了一种基于改进遗传算法的车辆路径调度算法,引入扬长避短的思想,对所有的车辆运输路径进行编码,并对所有的路径进行选择、交叉和变异运算,运用模拟退火算法提高算法的寻优性能,形成车辆的高效调度。实验结果表明,利用改进算法进行车辆调度,能够提高运输的效率,从而满足实际运输需求。With the increasing complexity of the transportation network, the transportation vehicles face the interference of traffic accident and congestion, etc. The traditional vehicle scheduling models lack the interference risk analysis mechanism. The accurate scheduling model cannot be established. When the interference occurs, the scheduling efficiency is reduced greatly. In order to avoid the problem, an improved vehicle routing scheduling algorithm is proposed based on improved genetic algorithm. The concept of making best use of the advantages and bypassing the disadvantages is introduced, all the vehicle transport paths are coded, and the selection, crossover and mutation operations are taken for all the transport paths. The simulated annealing algorithm is used to improve the algorithm performance, so efficient scheduling of vehicle is obtained. The experimental results show that the improved algorithm is applied in vehicle scheduling, the transportation efficiency is improved, and it can meet the practical needs of transportation.

关 键 词：车辆调度 货物运输 遗传算法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]