• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Winter Simulation Conference >Combining biased random sampling with metaheuristics for the facility location problem in distributed computer systems
【24h】

Combining biased random sampling with metaheuristics for the facility location problem in distributed computer systems

机译：分布式计算机系统中的设施位置问题的偏见随机抽样结合了偏见的随机抽样

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

摘要

This paper introduces a probabilistic algorithm for solving the well-known Facility Location Problem (FLP), an optimization problem frequently encountered in practical applications in fields such as Logistics or Telecommunications. Our algorithm is based on the combination of biased random sampling -using a skewed probability distribution- with a metaheuristic framework. The use of random variates from a skewed distribution allows to guide the local search process inside the metaheuristic framework which, being a stochastic procedure, is likely to produce slightly different results each time it is run. Our approach is validated against some classical benchmarks from the FLP literature and it is also used to analyze the deployment of service replicas in a realistic Internet-distributed system.
机译：本文介绍了解决知名设施定位问题（FLP）的概率算法，在物流或电信等领域的实际应用中经常遇到的优化问题。 我们的算法基于偏置随机采样的组合 - 使用偏斜概率分布 - 具有成群质框架。 使用随机变体从偏斜分布允许引导本地搜索过程在成群质框架内，这是一种随机过程，每次运行时都可能产生略微不同的结果。 我们的方法是针对FLP文献的一些古典基准验证的方法，也用于分析现实互联网分布式系统中的服务副本部署。

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