摘要
针对一类随机顾客车辆路径问题(Vehicle routing problem with stochastic customer,VRPSC),探讨了VRPSC的实时动态规划机制,并结合运送货物需求量的不同特性,分析了车辆供货中遇到服务路线失败时的两种不同服务策略并构建了相应的模型。设计了针对VRPSC的蚁群算法,并选用60个节点的基准问题对VRPSC的动态模型进行了仿真计算。结果表明,对顾客信息进行数据挖掘以获取较精确经验概率以及采用部分服务策略均有助于缩短车辆总行驶路径,为有效降低车辆的运行成本提供了科学依据。
To solve the vehicle routing problem with stochastic customer（VRPSC）,this paper discusses the real-time dynamic planning mechanism of VRPSC,combined with different characteristics of the demand of the transported goods,analyses two kinds of different service tactics when the vehicle meets the route fails.And the corresponding models are established.With the designed ant colony optimization for VRPSC it uses the benchmark of 60 nodes to test the dynamic models of VRPSC.Results indicate that the data excavate on customer information is carried on to obtain accurate experience probability and using partly service strategy to shorten the general vehicle routing.It offers a scientific basis for reducing the vehicle operating cost.
出处
《南京航空航天大学学报》
EI
CAS
CSCD
北大核心
2010年第4期521-525,共5页
Journal of Nanjing University of Aeronautics & Astronautics
基金
国家社会科学基金(09CJY074)资助项目
贵州省科技基金(黔科合J字[2009]2120[2010]2098)资助项目
贵州省教育厅自然科学重点(黔教科20090013)资助项目
贵州财经学院引进人才资助项目
关键词
车辆路径问题
随机顾客
蚁群算法
vehicle routing problem
stochastic customer
ant colony optimization
