Journal of Jilin University(Engineering and Technology Edition) ›› 2019, Vol. 49 ›› Issue (6): 2069-2075.doi: 10.13229/j.cnki.jdxbgxb20180674

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Balancing and optimization of robotic assemble lines withtool and space constraint

Bing-hai ZHOU(),Qiong WU   

  1. College of Mechanical Engineering, Tongji University, Shanghai 201804, China
  • Received:2018-06-25 Online:2019-11-01 Published:2019-11-08

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Abstract:

In order to improve the efficiency and productivity of the assembly line, the assembly line area was optimized and the space occupied by the tools and changeover for each task were taken into consideration when balancing the robotic assembly line. The two conflicting objective functions considered in this article were to minimize the cycle time of the number of workstations and to minimize the area of each workstation. A mathematical model was first established. In order to effectively solve this problem, a coding method based on separator genes was designed and then an improved multi-objective immune clonal algorithm was proposed for the constrained multi-objective optimization problem. Pareto frontier sorting, elite strategy, and global search were introduced to enhance the performance of the algorithm. Finally, the problems of different scales were optimized and the results were compared with other algorithms to evaluate the efficiency and superiority of the proposed algorithm. The results show that the algorithm is effective on both quantity and quality of solutions, and feasible for different scales of problems.

Key words: computer applications, robots, assembly line balancing, space constraint, multi-objective, immune clonal algorithm

CLC Number: 

  • TP391

Fig.1

Coding scheme example"

Fig.2

Crowding distance"

Table 1

Parameters of three algorithms"

算法 参 数
NSGA?II Q=100;P m=0.4;P c=0.6
MICA Q=100;P m=0.4;P c=0.6;μ=10
AMICA Q=100;P m=0.4;P c=0.6
μ=10;α=(0, 0.8);γ=40

Table 2

Numerical calculation results of different scale problems"

问题名称 NSGA?II MICA AMICA
N GD s T N GD s T N GD s T
小规模 WEEMAG (I=75,CT=56) 5 6.35 1.72 154 3 8.34 2.22 144 8 4.75 1.07 181
LUTZ2 (I=89，CT=16) 6 1.43 75.39 179 4 3.65 90.35 114 5 1.62 69.86 200
中规模 BARTHOLD (I=148， CT=805) 14 4.98 26.19 452 10 6.34 31.16 463 18 3.09 24.55 497
BARTHOL2 (I=148，CT=85) 4 5.06 11.32 356 3 8.86 13.28 299 6 3.88 8.29 398
大规模 SCHOLL (I=297，CT=1394) 8 52.45 8.29 398 7 60.23 185.37 492 11 32.18 97.91 862
NTIGen (I=320，CT=169.55) 8 3.98 7.70 723 6 5.02 10.31 693 11 1.77 5.23 1196
均 值 8.33 37.49 111.09 378.08 6.41 48.145 145.93 326.0 10.83 36.71 101.65 487.42

Fig.3

Box-plot of s and N of different algorithms under problem SCHOLL"

Fig.4

Effect of parameter α on performance of algorithm under problem NTIGen"

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