吉林大学学报(工学版) ›› 2020, Vol. 50 ›› Issue (5): 1809-1817.doi: 10.13229/j.cnki.jdxbgxb20190577

• 计算机科学与技术 • 上一篇    

基于线边集成超市的混流装配线动态物料配送调度

周炳海(),何朝旭   

  1. 同济大学 机械与能源工程学院, 上海 201804
  • 收稿日期:2019-06-08 出版日期:2020-09-01 发布日期:2020-09-16
  • 作者简介:周炳海(1965-),男,教授,博士生导师.研究方向:离散系统调度、建模与仿真.E-mail:bhzhou@tongji.edu.cn
  • 基金资助:
    国家自然科学基金项目(71471135)

Dynamic material handling scheduling for mixed⁃model assembly lines based on line⁃integrated supermarkets

Bing-hai ZHOU(),Zhao-xu HE   

  1. College of Mechanical Engineering, Tongji University, Shanghai 201804, China
  • Received:2019-06-08 Online:2020-09-01 Published:2020-09-16

摘要:

针对基于线边集成超市的混流装配线物料配送调度问题,提出了一种基于极限学习机和知识库的动态调度方法。首先,对动态物料配送调度问题进行描述,建立了数学模型,考虑设备的随机故障和装配时间的不稳定性,在产品配比和调度目标权重可变的条件下,最大化装配线产量和物流工人数的权重和;然后,构建了基于极限学习机和知识库的动态调度方法,考虑到极限学习机的缺陷,提出了基于精英反向学习的自适应差分进化算法优化极限学习机;最后,仿真实验结果证明了本文动态调度方法在动态调度过程中的可行性和有效性。

关键词: 计算机应用, 物料配送, 混流装配线, 线边集成超市, 动态调度, 差分进化算法, 极限学习机

Abstract:

In order to solve the material handling scheduling problem for mixed-model assembly lines based on line-integrated supermarkets, this paper presents an extreme learning machine and knowledge base-based dynamic scheduling method. First, the dynamic material handling scheduling problem is described and a mathematical model is established. Using this model, the weight sum of the output of the assembly line and the number of logistics workers is maximized under the condition of variable product ratio and weights of scheduling criteria. Also the random failure of the equipment and the instability of the cycle time are considered. Then, an extreme learning machine and knowledge base-based dynamic scheduling method are constructed. Considering the defects of extreme learning machine, an elite opposition learning self-adaptive differential evolution-based extreme learning machine is proposed. Finally, the simulation results prove the feasibility and effectiveness of the proposed dynamic scheduling method in the dynamic scheduling process.

Key words: computer application, material handling, mixed-model assembly lines, line-integrated supermarkets, dynamic scheduling, differential evolution algorithm, extreme learning machine

中图分类号: 

  • TP29

图1

线边集成超市布局图"

图2

ELM权重和偏置的编码方式"

图3

EOADE-ELM算法流程图"

图4

仿真模型局部界面"

表1

可选调度规则集"

调度规则类型μνπ
规则1MBS-1FRFPFRFD
规则2MBS-2SDFPSDFD
规则3MBS-3SSFPSSFD

图5

仿真实验结果"

图6

EKDSM与基本调度规则性能对比"

表2

神经网络性能对比"

组别EOADE?ELMDE?ELMGAP/%ELMGAP/%BPGAP/%
均值14 737 75414 726 1410.0814 718 2900.1314 731 1260.04
114 734 37814 722 7420.0814 704 6530.2014 726 5260.05
214 827 96114 806 9460.1414 816 4290.0814 830 726-0.02
314 976 74414 971 8260.0314 942 6670.2214 963 7960.09
414 831 03114 817 0350.0914 815 9260.1014 827 9920.02
514 550 68114 553 682-0.0214 547 9460.0214 552 466-0.01
614 425 12514 396 6010.2014 416 7390.0614 423 0680.01
715 072 64115 064 0570.0615 047 0560.1715 058 4920.09
814 160 96514 144 8650.1114 152 3870.0614 162 479-0.01
915 284 31115 279 4760.0315 258 4730.1715 276 4390.05
1014 513 70014 504 1780.0714 480 6260.2314 489 2740.20
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