• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>BioTechnology: An Indian Journal >WK-means and branch-boundmethod based for cloud logistics scheduling
【24h】

WK-means and branch-boundmethod based for cloud logistics scheduling

机译：基于WK-均值和分支约束方法的云物流调度

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The efficient and accurate logistics scheduling problem has become the bottleneck that impedes the e-commerce development of China. Cloudbased logistics can achieve resource sharing and centralized logistics scheduling, which is expected to fundamentally solve the problems encountered in logistics scheduling. However, the current research about cloud logistics scheduling is only the beginning. Most methods based on exact algorithms and heuristic scheduling algorithms are timeconsuming and inefficientwhile efficient scheduling algorithmis relatively scarce. This article takes cloud logistics scheduling problemas a NP-hard problem with multi-constraint and multi-objective decision making and establishes a multi-objective optimization cloud logistics scheduling model. K-means algorithmis used to cluster large and complex distribution network, but due to the load balancing problem in practical application, we useWK-means cluster which take the weight as an external constraints to balance the workload between each cluster. Large-scale VRP problem will eventually be divided into point-to-point TSP problemwhich we can use the branch-bound to solve and optimize. Simulation results show that the proposed scheme is more accurate and efficient than the existing typical heuristic scheduling method.
机译：高效，准确的物流调度问题已成为制约中国电子商务发展的瓶颈。基于云的物流可以实现资源共享和集中式物流调度，有望从根本上解决物流调度中遇到的问题。但是，当前关于云物流调度的研究仅仅是开始。大多数基于精确算法和启发式调度算法的方法既耗时又效率低下，而高效的调度算法却相对匮乏。本文将云物流调度问题作为具有多约束，多目标决策的NP难问题，建立了多目标优化的云物流调度模型。 K-means算法用于对大型复杂的配电网络进行集群，但是由于实际应用中的负载均衡问题，我们使用WK-means集群，该WK-means集群以权重为外部约束来平衡每个集群之间的工作量。大规模的VRP问题最终将分为点对点TSP问题，我们可以使用分支约束来解决和优化问题。仿真结果表明，该方案比现有的典型启发式调度方法更加准确，高效。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号