计算机工程 ›› 2019, Vol. 45 ›› Issue (10): 26-32.doi: 10.19678/j.issn.1000-3428.0053218

所属专题: 云计算专题

• 云计算专题 • 上一篇    下一篇

云制造环境下基于改进NSBBO的任务调度算法

郑楚红a,b, 彭勇a,b, 徐一鸣a,b, 廖毅a,b   

  1. 江南大学 a. 物联网工程学院;b. 物联网技术应用教育部工程研究中心, 江苏 无锡 214122
  • 收稿日期:2018-11-23 修回日期:2019-01-24 出版日期:2019-10-15 发布日期:2019-10-16
  • 作者简介:郑楚红(1992-),女,硕士研究生,主研方向为云资源调度、智能制造;彭勇,副教授;徐一鸣、廖毅,硕士研究生。
  • 基金项目:
    江苏省产学研联合创新资金(BY2013015-35);江苏省交通运输厅项目(2012X08-2)。

Task Scheduling Algorithm Based on Improved NSBBO in Cloud Manufacturing Environment

ZHENG Chuhonga,b, PENG Yonga,b, XU Yiminga,b, LIAO Yia,b   

  1. a. School of Internet of Things Engineering;b. Engineering Research Center of Internet of Things Technology Applications, Ministry of Education, Jiangnan University, Wuxi, Jiangsu 214122, China
  • Received:2018-11-23 Revised:2019-01-24 Online:2019-10-15 Published:2019-10-16

摘要: 针对云制造环境下的多目标任务调度问题,改进非支配排序生物地理优化算法,提出一种反映用户偏好的任务调度算法(UPTSA)。通过基于权重均匀分配策略定义的用户偏好度来评估制造任务调度方案的质量,使UPTSA算法能寻找反映用户偏好的最优解,并设计梯形迁移率计算模型扩大其搜索邻域,避免陷入局部最优解。实例分析结果表明,UPTSA算法能有效求解云制造环境下的多目标任务调度问题,为用户提供一组辅助其决策的调度方案,从而满足高度个性化的用户需求。

关键词: 云制造, 非支配排序生物地理优化算法, 用户偏好, 任务调度算法, 权重均匀分配策略, 迁移率

Abstract: In order to solve multi-objective task scheduling problem in cloud manufacturing environment,this paper proposes a User Preference Task Scheduling Algorithm(UPTSA) through improving Non-dominated Sorting Biogeography-based Optimization(NSBBO) algorithm.The quality of the manufacturing task scheduling scheme is evaluated by the user preference defined by the uniform weight allocation strategy,so that the UPTSA algorithm can find the optimal solution reflecting the user's preference,and the trapezoidal migration rate calculation model is designed to expand the search neighborhood and avoid falling into the local maximum.The example analysis results show that UPTSA algorithm can effectively solve the multi-objective task scheduling problem in cloud manufacturing environment,and provide users with a set of scheduling schemes to assist their decision-making,so as to meet highly personalized user requirements.

Key words: cloud manufacturing, Non-dominated Sorting Biogeography-based Optimization(NSBBO) algorithm, user preference, task scheduling algorithm, uniform weight allocation strategy, migration rate

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