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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2022, Vol. 56 Issue (6): 1097-1106    DOI: 10.3785/j.issn.1008-973X.2022.06.006
    
Joint decision-making of balancing and sequencing for type-II robotic mixed-model assembly line
Bao-feng SUN1(),Xin-kang ZHANG1,Gen-dao LI2,*(),Jiao-jiao LIU1
1. College of Transportation, Jilin University, Changchun 130022, China
2. School of Economics and Management, Changchun University of Science and Technology, Changchun 130012, China
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Abstract  

In order to optimize the energy and efficiency of the type-II robotic mixed-model assembly line system, firstly, five working states of industrial robots was defined to measure the robot energy consumption in state. A dual-objective optimization model of joint decision-making of balancing and sequencing was proposed in consideration of shutdown strategies and product switching. Its objectives were taken as minimizing the maximum working time and energy consumption. Next, the improved non-dominated sorting genetic algorithm II (NSGA-II) was designed and its validation was also verified by comparing with a similarly algorithm. Finally, the technical impact of shutdown strategy and product switching on the robotic mixed-model assembly line system was shown by example analysis. The shutdown strategy was able to bring energy savings to a certain extent, and the benefits become more obvious when the balance of the assembly line decreases, and the maximum energy saving rate reached 16.68%; the product switching in consideration was beneficial both to the operation efficiency and energy efficiency for the robotic mixed-model assembly line.

Key words： type-II robotic mixed-model assembly line system      energy consumption      shutdown strategy      product switching      balancing and sequencing optimization      improved non-dominated sorting genetic algorithm II     
Received: 12 September 2021      Published: 30 June 2022
CLC:  TP 278  
  TN 805  
Fund:  国家自然科学基金资助项目（61873109)；吉林省自然科学基金资助项目（20210101055JC）；一汽股份技术创新资助项目（KF2020-20006）
Corresponding Authors: Gen-dao LI     E-mail: sunbf@jlu.edu.cn;gendaoli@cust.edu.cn
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Bao-feng SUN
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Cite this article:

Bao-feng SUN,Xin-kang ZHANG,Gen-dao LI,Jiao-jiao LIU. Joint decision-making of balancing and sequencing for type-II robotic mixed-model assembly line. Journal of ZheJiang University (Engineering Science), 2022, 56(6): 1097-1106.

URL:

https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2022.06.006     OR     https://www.zjujournals.com/eng/Y2022/V56/I6/1097


第II类机器人混流装配线的平衡与排序联合决策

为了提高第II类机器人混流装配线系统能效，界定工业机器人5种工作状态，量化不同状态下的机器人能源消耗值，引入关停策略和考虑产品切换，以最小化最大工作时间和能源消耗为目标，构建平衡与排序联合决策双目标优化模型. 设计改进的非支配排序遗传算法II，通过同类算法对比分析，验证改进算法的有效性. 结合算例，揭示关停策略和产品切换准备作业对机器人混流装配线系统的技术影响。关停策略能够减少能耗，在装配线平衡性下降时的效果愈加明显，最大能耗节约率达到16.68%；考虑产品切换准备作业的影响，有利于机器人混流装配线作业效率和能源消耗的整体优化.


关键词： 机器人混流装配线系统,  能源消耗,  关停策略,  产品切换,  平衡与排序优化,  改进的分支配排序遗传算法II 
Fig.1 Schematic diagram of integrated assembly sequence
Fig.2 Relationship of process vector, allocation vector and workstation
Fig.3 Encoding vectors and decoding result
Fig.4 Crossover of sequence vector based on FRC
Fig.5 Crossover of robot allocation vector, product sequence vector in MPS based on OX
Fig.6 Mutation of sequence vector
Fig.7 Neighbor search of sequence vector
$ \eta $ $ N $ $ D $ $ M $ $ H $
1 19 3 3 1,1,1
2 19 3 3 3,2,1
3 19 3 4 1,1,1
4 19 3 4 3,2,1
5 61 4 5 1,1,1,1
6 61 4 5 1,3,4,5
7 61 4 7 1,1,1,1
8 61 4 7 1,3,4,5
9 61 4 10 1,1,1,1
10 61 4 10 1,3,4,5
11 111 5 9 1,1,1,1,1
12 111 5 9 1,2,4,5,8
13 111 5 9 5,3,2,1,1
14 111 5 9 1,4,8,3,1
15 111 5 13 1,1,1,1,1
16 111 5 13 1,2,4,5,8
17 111 5 13 5,3,2,1,1
18 111 5 13 1,4,8,3,1
19 111 5 15 1,1,1,1,1
20 111 5 15 1,2,4,5,8
Tab.1 Table of benchmark date (part)
水平 $ {s_{\text{p}}} $ $ {n_{\max }} $ $ {p_{\text{c}}} $ $ {p_{\text{m}}} $
1 0.04 0.45 0.22 1.09
2 0.49 0.74 0.69 0.65
3 0.20 0.80 0.78 0.25
4 1.76 0.51 0.80 0.51
$ {R_{\text{V}}} $极差 1.72 0.36 0.58 0.85
$ {R_{\text{V}}} $等级 1.00 4.00 3.00 2.00
Tab.2 Ranking for mean and significance of response variable
$\eta $ $ \overline {{N_{\text{m}}}} $ $ \overline C $ $ \overline S $ $ \overline T /s $
HPSA NSGA-II (HPSA, NSGA-II) (NSGA-II, HPSA) HPSA NSGA-II HPSA NSGA-II
1 13.50 13.20 0.46 0.37 0.25 0.27 46.29 19.09
2 7.40 3.70 0.56 0.29 1.41 1.53 53.09 26.87
3 3.20 2.90 0.63 0.19 0.24 0.03 42.51 26.23
4 14.80 13.70 0.51 0.30 1.24 0.56 42.32 27.56
5 11.20 15.80 0.23 0.42 3.72 3.54 90.45 48.91
6 45.00 55.80 0.17 0.59 12.85 9.35 89.65 47.45
7 35.30 54.40 0.21 0.56 3.62 2.99 98.87 47.19
8 31.80 61.40 0.13 0.65 12.01 11.15 99.74 48.34
9 15.60 15.10 0.28 0.44 2.90 5.99 118.65 57.62
10 23.80 29.00 0.20 0.45 16.84 15.62 119.83 56.50
11 32.90 50.40 0.21 0.58 10.99 6.51 205.42 119.77
12 31.70 63.90 0.10 0.55 39.93 27.88 186.05 123.80
13 39.10 76.10 0.13 0.63 26.95 16.22 183.14 120.85
14 34.50 79.50 0.12 0.57 30.55 18.55 184.47 122.53
15 22.40 21.60 0.24 0.34 10.79 8.69 196.36 130.17
16 24.80 42.60 0.15 0.58 38.35 27.07 202.19 130.60
17 27.20 34.80 0.07 0.67 21.25 18.09 199.51 127.65
18 25.60 53.50 0.15 0.65 32.87 25.56 202.00 129.96
19 18.70 22.30 0.16 0.43 8.75 11.78 202.06 132.87
20 21.50 42.50 0.25 0.45 44.03 35.24 211.06 133.63
Tab.3 Average of NSGA-II and HPSA performance indexes under benchmark data
Fig.8 Average number of non-dominated solutions of algorithms under benchmark data
Fig.9 Average coverage rate of algorithms under benchmark data
Fig.10 Average spacing of algorithms under benchmark data
Fig.11 Average run time of algorithms under benchmark data
Fig.12 Distribution of Pareto solution with and without shutdown strategy
Fig.13 Comparison of energy saving rate with and without shutdown strategy
P A 方案1 方案2 $ R $/%
F 1 F 2 F 1 F 2 F 1 F 2
1 20.30 141.53 16.68 131.67 17.83 6.97
2 — — 16.72 131.61 17.63 7.01
3 — — 16.90 131.38 16.74 7.17
4 — — 16.92 130.81 16.64 7.57
5 — — 17.05 130.22 16.00 7.99
平均值 20.30 141.53 16.86 131.14 16.97 7.34
Tab.4 Comparison of energy and efficiency optimization with and without product switching
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