Journal of Jilin University(Engineering and Technology Edition) ›› 2020, Vol. 50 ›› Issue (5): 1809-1817.doi: 10.13229/j.cnki.jdxbgxb20190577

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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

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

CLC Number: 

  • TP29

Fig.1

Layout of line-integrated supermarkets"

Fig.2

Coding scheme of w and b"

Fig.3

Flowchart of EOADE-ELM algorithm"

Fig.4

Partial interface of simulation model"

Table 1

Set of optional scheduling rules"

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

Fig. 5

Simulation experiment results"

Fig.6

Performance of EKDSM and basic scheduling rules"

Table 2

Neural network performance comparison"

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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