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一种基于协同进化的流水线向Seru系统转化方法

吴旭辉 杜劭峰 郝慧慧 于洋 殷勇 李冬妮

吴旭辉, 杜劭峰, 郝慧慧, 于洋, 殷勇, 李冬妮. 一种基于协同进化的流水线向Seru系统转化方法. 自动化学报, 2018, 44(6): 1015-1027. doi: 10.16383/j.aas.2018.c160642
引用本文: 吴旭辉, 杜劭峰, 郝慧慧, 于洋, 殷勇, 李冬妮. 一种基于协同进化的流水线向Seru系统转化方法. 自动化学报, 2018, 44(6): 1015-1027. doi: 10.16383/j.aas.2018.c160642
WU Xu-Hui, DU Shao-Feng, HAO Hui-Hui, YU Yang, YIN Yong, LI Dong-Ni. A Line-seru Conversion Approach by Means of Cooperative Coevolution. ACTA AUTOMATICA SINICA, 2018, 44(6): 1015-1027. doi: 10.16383/j.aas.2018.c160642
Citation: WU Xu-Hui, DU Shao-Feng, HAO Hui-Hui, YU Yang, YIN Yong, LI Dong-Ni. A Line-seru Conversion Approach by Means of Cooperative Coevolution. ACTA AUTOMATICA SINICA, 2018, 44(6): 1015-1027. doi: 10.16383/j.aas.2018.c160642

一种基于协同进化的流水线向Seru系统转化方法

doi: 10.16383/j.aas.2018.c160642
基金项目: 

特种车辆及其传动系统智能制造国家重点实验室开放课题 GZ2016KF003

国家自然科学基金 71401014

详细信息
    作者简介:

    吴旭辉  北京理工大学计算机学院硕士研究生.主要研究方向为演化计算和生产调度.E-mail:harry_wxh@163.com

    杜劭峰  特种车辆及其传动系统智能制造国家重点实验室主任.主要研究方向为数字化与智能制造

    郝慧慧 特种车辆及其传动系统智能制造国家重点实验室成员.主要研究方向为数字化仿真技术

    于洋 东北大学信息科学与工程学院副教授.主要研究方向为工业工程, 绿色物流.E-mail:yuyang@ise.neu.edu.cn

    殷勇  同志社大学大学院商学研究科教授.主要研究方向为Seru制造与工业4.0.E-mail:yyin@mail.doshisha.ac.jp

    通讯作者:

    李冬妮 北京理工大学计算机学院副教授.主要研究方向为智能优化, 企业计算, 物流管理.本文通信作者.E-mail:ldn@bit.edu.cn

A Line-seru Conversion Approach by Means of Cooperative Coevolution

Funds: 

State Key Laboratory of Smart Manufacturing for Special Vehicles and Transmission Systems GZ2016KF003

National Natural Science Foundation of China 71401014

More Information
    Author Bio:

    Master student at the School of Computer Science, Beijing Institute of Technology. His research interest covers evolutionary computation and production scheduling

    Directory of State Key Laboratory of Smart Manufacturing for Special Vehicles and Transmission System. His research interest covers digitization and smart manufacturing

    Member of State Key Laboratory of Smart Manufacturing for Special Vehicles and Transmission System. Her main research interest is digital simulation

    Associate professor at the School of Information Science and Engineering, Northeastern University. His research interest covers green logistics and industrial engineering

    Professor at the Graduate School of Business, Doshisha University. His research interest covers production and Industry 4.0

    Corresponding author: LI Dong-Ni Associate professor at the School of Computer Science, Beijing Institute of Technology. Her research interest covers intelligent optimization, enterprise computation, and logistics management. Corresponding author of this paper
  • 摘要: Seru生产系统是一种被广泛应用于电子制造产业的新型生产模式,但由于流水线向Seru系统转化问题(Line-seru conversion)包含有Seru构建与Seru调度两个相互耦合的子问题,现有算法难以在同时兼顾解的质量与计算效率的情况下对问题进行求解.因此,本文针对流水线向Seru系统转化问题的特点,提出了一种协同进化算法,即在进化算法中加入了协同机制,将Seru构建与Seru调度子问题作为两个子种群利用该机制进行协同进化,从而弥补了现有算法的不足.并且,本文还针对问题特点设计了个体基因编码方式,从而使规划获得的Seru生产系统具有更优的生产性能及均衡性能.实验表明,采用加入了协同机制的进化算法比传统解决流水线向Seru系统转化问题的方法具有更好的性能,本文所提的方法在最小化产品流通时间和劳动时间有较好的性能表现,并且具有较高的计算效率.
    1)  本文责任编委 宋士吉
  • 图  1  Seru构建编码示例1

    Fig.  1  Example 1 of coding for seru formation

    图  2  Seru构建编码示例2

    Fig.  2  Example 2 of coding for seru formation

    图  3  Seru调度编码示例1

    Fig.  3  Example 1 of coding for seru loading

    图  4  Seru调度编码示例2

    Fig.  4  Example 2 of coding for seru loading

    图  5  交换前编码示例

    Fig.  5  Example of coding before exchange

    图  6  交换后编码示例

    Fig.  6  Example of coding after exchange

    图  7  协同进化图

    Fig.  7  Diagram of cooperative coevolution

    图  8  工人数量为5时, MOCC与未加入协同算法的非支配集间的比较

    Fig.  8  The non-dominated solutions of MOCC and the one without cooperation strategy with 5 workers

    图  9  工人数量为5时, MOCC与NSGA-Ⅱ算法的非支配集间的比较

    Fig.  9  The non-dominated solutions of MOCC and NSGA-Ⅱ with 5 workers

    图  10  工人数量为5时, MOCC与加入局部搜索的NSGA-Ⅱ算法的非支配集间的比较

    Fig.  10  The non-dominated solutions of MOCC and NSGA-Ⅱ combining local search with 5 workers

    表  1  算例产生的参数表

    Table  1  Parameters of test problems

    算例产生参数取值
    产品类型5
    批次大小 $\sim$ U[10, 110]
    $\varepsilon$${}_{i}$ $\sim$ N[0.2, 0.05]
    SL${}_{n}$2.2
    SCP${}_{n}$1.0
    $T{}_{n}$1.8
    $\eta$${}_{i}$10
    下载: 导出CSV

    表  2  与未使用协同策略的性能对比

    Table  2  Comparison proposed approach and the one without cooperation strategy

    $W$${R}$Proposed algorithmMOE${\rm Gap}_{{\rm RNI}\_AV}$(%)${\rm Gap}_{D\_AV}$(%)
    $Av$ RNIMin RNI$Av$ $D_{av}$$Av$ $D_{\max}$$Av$ RNIMin RNI$Av$ $D_{av}$$Av$ $D_{\max}$
    5200.660.500.040.090.420.350.130.3757.14256.83
    10430.500.330.120.680.450.350.160.8110.0036.63
    15620.510.320.030.220.400.450.060.4827.50117.42
    20570.650.470.020.130.410.350.050.3656.89169.32
    25310.640.540.110.260.520.440.160.2122.9649.06
    30390.690.560.090.280.410.460.180.3245.5787.23
    Average36.68119.42
    注: $W$表示工人数量, $R$表示参考集中解的数量
    下载: 导出CSV

    表  3  与NSGA-Ⅱ方法的性能对比

    Table  3  Comparison of proposed approach and NSGA-Ⅱ

    ${W}$${R}$Proposed algorithmNSGA-Ⅱ${\rm Gap}_{{\rm RNI}\_AV}$(%)${\rm Gap}_{D\_AV}$(%)${{\rm Gap}_{\rm STDEV}}$
    $Av$ RNI$Av$ $D_{av}$$Av$ $D_{\max}$STDEV TTPT$Av$ RNI$Av$ $D_{av}$$Av$ $D_{\max}$STDEV TTPT
    5200.660.040.09134.910.370.140.40390.9578.38281.42189.79
    10430.450.120.68330.330.350.140.691 101.2825.7120.61233.39
    15620.400.030.22552.930.270.060.452 518.0148.71142.42355.39
    20570.650.020.131 672.870.230.060.385 443.23176.02263.64225.38
    25310.640.110.261 551.910.490.180.317 613.6331.5273.58390.60
    30390.690.090.281 286.320.410.190.276 156.8766.6897.87378.64
    Average71.17146.59295.53
    下载: 导出CSV

    表  4  与加入local search的NSGA-Ⅱ方法的性能对比

    Table  4  Comparison of proposed approach and NSGA-Ⅱ combining local search

    ${W}$${R}$Proposed algorithmNSGA-Ⅱ combining local search${\rm Gap}_{{\rm RNI}\_AV}$(%)${\rm Gap}_{D\_AV}$(%)${{\rm Gap}_{\rm STDEV}}$
    $Av$ RNI$Av$ $D_{av}$$Av$ $D_{\max}$STDEV TTPT$Av$ RNI$Av$ $D_{av}$$Av$ $D_{\max}$STDEV TTPT
    5200.660.040.09134.910.400.130.42377.0865.00262.84179.51
    10430.450.120.68330.330.390.150.681 010.4815.6826.75205.90
    15620.400.030.22552.930.260.070.523 122.8153.92159.85464.78
    20570.650.020.131 672.870.290.060.395 275.38122.79263.64215.35
    25310.640.110.261 551.910.510.170.266 962.8326.3564.15348.66
    30390.690.090.281 286.320.470.160.346 261.2647.4474.47386.76
    Average55.20141.95300.16
    下载: 导出CSV

    A1  工人i的多能工系数

    A1  Worker i's coefficient of influencing level of doing multiple assembly task

    工人12345
    $\varepsilon{}_{i}$0.180.190.20.210.2
    工人678910
    $\varepsilon{}_{i}$0.20.20.220.190.19
    工人1112131415
    $\varepsilon{}_{i}$0.180.230.240.220.16
    工人1617181920
    $\varepsilon{}_{i}$0.240.180.180.210.18
    下载: 导出CSV

    A2  工人对不同类型产品熟练度数据的分布

    A2  The data distribution of worker's level of skill for each product type

    产品类型
    12345
    N(1, 0.05)N(1.05, 0.05)N(1.1, 0.05)N(1.15, 0.05)N(1.2, 0.05)
    下载: 导出CSV

    A3  工人对不同产品的熟练度

    A3  The data of worker's level of skill

    工人/产品12345
    10.920.961.041.091.20
    20.950.971.091.121.18
    30.991.011.051.091.21
    41.031.071.091.121.25
    50.961.021.051.101.18
    61.011.101.101.151.23
    71.041.071.091.171.24
    80.981.021.101.111.20
    90.971.031.121.191.26
    100.981.061.131.181.28
    110.951.041.031.141.19
    120.981.071.071.151.15
    130.990.951.111.171.10
    141.011.101.051.131.18
    151.041.101.051.151.11
    160.990.971.081.111.22
    171.041.011.111.151.24
    180.931.061.071.131.14
    190.960.981.121.141.21
    201.081.041.091.111.13
    下载: 导出CSV

    A4  30批产品的信息数据

    A4  The data of 30 batches

    批次编号产品类型批次大小
    1346
    2568
    3345
    4419
    5136
    6445
    7162
    8230
    9260
    10367
    1129
    12424
    13338
    14432
    15552
    16548
    17168
    18471
    19246
    20525
    21126
    22352
    23446
    24544
    25232
    26375
    27133
    284103
    29274
    30353
    下载: 导出CSV
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出版历程
  • 收稿日期:  2016-09-08
  • 录用日期:  2017-07-12
  • 刊出日期:  2018-06-20

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