求解VRPTW问题的多目标模糊偏好蚁群算法  

VRPTW problem solving multi-objective fuzzy preference ant colony algorithm

作　　者：李世威 王建强[1] 曾俊伟[1] 

机构地区：[1]兰州交通大学交通运输学院,兰州730070

出　　处：《计算机应用研究》2011年第12期4495-4499,共5页Application Research of Computers

基　　金：国家社会科学基金资助项目(08XTQ010);甘肃省自然科学基金资助项目(096RJZA088)

摘　　要：通过分析多目标的、有时间窗的车辆路径问题,对各个目标进行多属性模糊评判,结合相关专家的综合意见以及决策者自身对专家意见的偏好,将决策者对目标属性的离散意见转换为对各目标的综合意见;通过定义一种模糊综合排序指标来确定决策者对各目标的偏好权重,依据目标权重和各目标函数的规范化处理值,构建评价有时间窗的车辆路径问题的多目标模糊综合适应度函数;采用最大—最小蚂蚁系统算法对该问题进行求解;最后通过一个算例来说明该算法的有效性。By analyzing the multi-objective vehicle routing problem with time window, it fuzzy evaluated multi-attributes of each objective, combined the comprehensive views of the relevant decision-makers, and transferred discrete levels of objective＇ s attributes to integrated levels. After that, it defined a fuzzy integrated index to determine each objective sorting weight, and determined the multi-objective fuzzy fitness function of vehicle routing problem with time window base on objective＇ s weights and standardized objective function value. Then it used max-min ant system algorithm to solve the problem. Finally, .it used a case to illustrate the algorithm＇s effectiveness. m

关 键 词：车辆路径问题 时间窗 多目标 模糊效用 模糊评价 蚁群算法 最大-最小蚂蚁系统 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]