欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
突发性事件中应急物资调度方案最优化问题是典型的车辆路径规划（VRP）问题。对于大规模的VRP问题求解,经典的启发式算法易陷入局部最优,难以得到高质量的调度方案。针对这一问题,提出了一种基于K均值聚类和LK算法的调度方法。该方法采用K均值聚类方法将需求节点分成n个子集合,对聚类结果进行修正后分配给n辆运输车辆,采用LK算法对每辆运输车辆的运输路径进行优化。仿真实验结果表明,方法获得了较好的调度方案,而且单个运输车辆服务的需求节点个数越多,方法的优势越明显。
英文摘要：
Emergency supplies scheduling in large-scale emergency is a classical Vehicle Routing Problem（VRP）.For large-scale VRP,traditional heuristic algorithms are easy to fall into local optimal and hard to obtain high quality scheduling scheme.To remedy this,a scheduling algorithm based on K-mean cluster and LK algorithm is proposed.K-mean cluster is used to divide the demand nodes into n subsets,after some regulations these subsets of demand nodes are dispatched to n vehicles.The routing of each vehicle is optimized by LK algorithm.Experimental results indicate that,the proposed algorithm can obtain better scheduling schemes.Moreover,the larger the number of demand nodes served by a single vehicle,the more outstanding superiority of the proposed algorithm.
同期刊论文项目
同项目期刊论文
期刊信息
位置：