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一种多层次制造服务建模和组合优选方法

丁涛 闫光荣 雷毅 徐翔宇

丁涛, 闫光荣, 雷毅, 等 . 一种多层次制造服务建模和组合优选方法[J]. 北京航空航天大学学报, 2019, 45(7): 1398-1405. doi: 10.13700/j.bh.1001-5965.2018.0630
引用本文: 丁涛, 闫光荣, 雷毅, 等 . 一种多层次制造服务建模和组合优选方法[J]. 北京航空航天大学学报, 2019, 45(7): 1398-1405. doi: 10.13700/j.bh.1001-5965.2018.0630
DING Tao, YAN Guangrong, LEI Yi, et al. A method of multi-level manufacturing service modeling and combinatorial optimal-selection[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(7): 1398-1405. doi: 10.13700/j.bh.1001-5965.2018.0630(in Chinese)
Citation: DING Tao, YAN Guangrong, LEI Yi, et al. A method of multi-level manufacturing service modeling and combinatorial optimal-selection[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(7): 1398-1405. doi: 10.13700/j.bh.1001-5965.2018.0630(in Chinese)

一种多层次制造服务建模和组合优选方法

doi: 10.13700/j.bh.1001-5965.2018.0630
基金项目: 

国家科技重大专项 2018ZX04001006

详细信息
    作者简介:

    丁涛   男, 博士研究生。主要研究方向:智能协同制造、云制造、制造过程管理等

    闫光荣   男, 博士, 副研究员。主要研究方向:智能协同制造、CAD/CAM、云制造等

    通讯作者:

    闫光荣, E-mail: yangr@buaa.edu.cn

  • 中图分类号: V219;TP391.9

A method of multi-level manufacturing service modeling and combinatorial optimal-selection

Funds: 

National Science and Technology Major Project 2018ZX04001006

More Information
  • 摘要:

    为提高云制造环境下服务建模和组合优选的准确性,首先将制造服务分多个层次进行描述,从资源服务、功能服务和流程服务3个层次进行建模。然后针对多层次服务模型,采用服务执行时间、服务花费成本和服务用户评价等因素构建服务组合优选的质量评估函数。为解决多层次服务的组合优选问题,提出一种改进引力搜索算法(NGSA),将小生境中的拥挤度因子和适应值共享技术引入传统引力搜索算法(GSA)以提高收敛速度和准确性。算例验证表明,相比传统的遗传算法(GA)和粒子群优化(PSO)算法,NGSA能在较短的时间内收敛,且最优解的匹配准确度更高。

     

  • 图 1  制造任务和服务多层次特点

    Figure 1.  Multi-level characteristic of manufacturing tasks and services

    图 2  多层次服务建模和组合优选过程

    Figure 2.  Multi-level service modeling and optimal-selection process

    图 3  流程服务有向无环图

    Figure 3.  Directed acyclic graph of process services

    图 4  NGSA层间迁移流程图

    Figure 4.  Flowchart of interlayer migration of NGSA

    图 5  三种智能算法的迭代趋势

    Figure 5.  Iteration trend of three intelligent algorithms

    表  1  多层次服务的质量评估函数

    Table  1.   QoS functions for multi-level services

    评估指标 质量评估函数
    ST
    SC
    SE
    下载: 导出CSV

    表  2  三种智能算法的时间性能

    Table  2.   Time performance of three intelligent algorithms

    任务集/服务集 算法 最优适应度 最差适应度 平均适应度 平均迭代时间/ms
    10任务/100服务 NGSA 100.002 75.037 5 96.181 31.48
    GA 99.994 2 17.604 3 82.171 1 23.12
    PSO 100.002 65.250 3 93.289 5 45.24
    10任务/500服务 NGSA 100.003 78.217 2 96.056 7 43.97
    GA 101.146 17.988 72 82.016 4 37.6
    PSO 100.004 68.762 94.063 9 51.7
    30任务/100服务 NGSA 199.985 167.485 190.416 75.6
    GA 199.981 59.179 168.23 78.08
    PSO 200.001 140.691 187.58 109.03
    30任务/500服务 NGSA 200 146.342 193.048 99.4
    GA 203.228 19.734 3 165.127 114.88
    PSO 200.002 143.183 186.944 158.12
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-11-05
  • 录用日期:  2019-02-22
  • 刊出日期:  2019-07-20

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