...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International Journal of Production Research >Performance computation methods for composition of tasks with multiple patterns in cloud manufacturing
【24h】

Performance computation methods for composition of tasks with multiple patterns in cloud manufacturing

机译：云制造中多种模式的任务组成性能计算方法

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Task composition in cloud manufacturing involves the selection of appropriate services from the cloud manufacturing platform and combining them to process the task with the purpose of achieving its expected performance. Calculation methods for achieving the performance expected by customers when the task has two or more composition patterns (e.g. sequential and switching pattern) are necessary because most tasks have multiple composition patterns in cloud manufacturing. Previous studies, however, have focused only on a single composition pattern. In this paper, we regard a task as a directed acyclic graph, and propose graph-based algorithms to obtain cost, execution time, quality and reliability of a task having multiple composition patterns. In addition, we model the task composition problem by introducing cost and execution time as performance attributes, and quality and reliability as basic attributes in the Kano model. Finally, an experiment to compare the performances of three metaheuristic algorithms (namely, variable neighbourhood search, genetic, and simulated annealing) is conducted to solve the problem. The experimental result shows that the variable neighbourhood search algorithm yields better and more stable solutions than the genetic algorithm and simulated annealing algorithms.
机译：云制造中的任务组合涉及从云制造平台选择适当的服务，并将它们组合以处理任务，以实现其预期性能。当任务具有两个或更多个组成模式时，为客户预期的计算方法（例如，顺序和切换模式）是必要的，因为大多数任务都有多个云制造中的组成模式。然而，以前的研究仅集中在单一的成分模式上。在本文中，我们将任务视为定向的非循环图，并提出基于图形的算法，以获得具有多个组成模式的任务的成本，执行时间，质量和可靠性。此外，我们通过将成本和执行时间作为性能属性引入性能和执行时间来模拟任务构图问题，以及KANO模型中的基本属性的质量和可靠性。最后，进行了比较三种成群质算法的性能的实验（即可变邻域搜索，遗传和模拟退火）以解决问题。实验结果表明，变量邻域搜索算法比遗传算法和模拟退火算法产生更好更稳定的解决方案。

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