• 综合
  • 标题
  • 关键词
  • 摘要
  • 学者
  • 期刊-刊名
  • 期刊-ISSN
  • 会议名称
搜索

作者:

Dong, Tingting (Dong, Tingting.) | Xue, Fei (Xue, Fei.) | Xiao, Chuangbai (Xiao, Chuangbai.) | Li, Juntao (Li, Juntao.)

收录:

EI Scopus SCIE

摘要:

Cloud manufacturing promotes the transformation of intelligence for the traditional manufacturing mode. In a cloud manufacturing environment, the task scheduling plays an important role. However, as the number of problem instances increases, the solution quality and computation time always go against. Existing task scheduling algorithms can get local optimal solutions with the high computational cost, especially for large problem instances. To tackle this problem, a task scheduling algorithm based on a deep reinforcement learning architecture (RLTS) is proposed to dynamically schedule tasks with precedence relationship to cloud servers to minimize the task execution time. Meanwhile, the Deep-Q-Network, as a kind of deep reinforcement learning algorithms, is employed to consider the problem of complexity and high dimension. In the simulation, the performance of the proposed algorithm is compared with other four heuristic algorithms. The experimental results show that RLTS can be effective to solve the task scheduling in a cloud manufacturing environment.

关键词:

task scheduling Deep-Q-Network cloud manufacturing deep reinforcement learning

作者机构:

  • [ 1 ] [Dong, Tingting]Beijing Univ Technol, Fac Informat Technol, Beijing, Peoples R China
  • [ 2 ] [Xiao, Chuangbai]Beijing Univ Technol, Fac Informat Technol, Beijing, Peoples R China
  • [ 3 ] [Xue, Fei]Beijing Wuzi Univ, Sch Informat, Beijing, Peoples R China
  • [ 4 ] [Li, Juntao]Beijing Wuzi Univ, Sch Informat, Beijing, Peoples R China

通讯作者信息:

  • [Xue, Fei]Beijing Wuzi Univ, Sch Informat, Beijing, Peoples R China

电子邮件地址:

查看成果更多字段

相关关键词:

相关文章:

来源 :

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE

ISSN: 1532-0626

年份: 2020

期: 11

卷: 32

2 . 0 0 0

JCR@2022

ESI学科: COMPUTER SCIENCE;

ESI高被引阀值:132

被引次数:

WoS核心集被引频次: 78

SCOPUS被引频次: 94

ESI高被引论文在榜: 0 展开所有

万方被引频次:

中文被引频次:

近30日浏览量: 0

归属院系:

在线人数/总访问数:186/4674448
地址:北京工业大学图书馆(北京市朝阳区平乐园100号 邮编:100124) 联系我们:010-67392185
版权所有:北京工业大学图书馆 站点建设与维护:北京爱琴海乐之技术有限公司