Journal of Textile Research ›› 2022, Vol. 43 ›› Issue (12): 144-150.doi: 10.13475/j.fzxb.20211100208

• Apparel Engineering • Previous Articles     Next Articles

Balance optimization of clothing mass customization production line based on genetic algorithm

CHEN Sha, XIU Yi(), LI Xuefei   

  1. Information Center, Beijing Institute of Fashion Technology, Beijing 100029, China
  • Received:2021-11-01 Revised:2022-06-17 Online:2022-12-15 Published:2023-01-06
  • Contact: XIU Yi E-mail:xiuyiks@126.com

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Abstract:

In order to solve the problems of long product online time, unbalanced tempo, and high production cost in the production process of mass-customized mixed assembly line, the research reported in this paper first analyzed the characteristics and problems of the complex production line of clothing mass customization, and constructed a hybrid production line balance optimization model with multi-subgroup management, multi-objective optimization and clothing single-piece customization. The research used the matrix principle to constrain the coding of the process, and established a decoding algorithm for station conversion. As a result, production balance of the entire mixed pipeline was achieved using the designed algorithms for roulette selection, two-point crossover and single-point mutation, and multi-objective fitness function. Mass customization production line of suits was taken as an example to demonstrate the use of MatLab technology for programming. The results show that the highest preparation efficiency of all sub-production lines can reach 93.93%, the average preparation efficiency of the total production line is 85.77%, which meets the requirements of the process planning set by the collaborating enterprise. It is indicated that the model established in this research can effectively solve the production balance problem of the mass customization production line.

Key words: genetic algorithm, clothing mass customization, workshop scheduling, production line balance, hybrid production line

CLC Number: 

  • TS941.19

Fig.1

Process constraints and working time data of clothing. (a) Order A; (b) Order B; (c) Order C; (d) Consolidate order"

Fig.2

GA basic framework"

Fig.3

Cross operation"

Fig.4

Mutation operation"

Tab.1

Suit order information form"

订单编号 前片包含定制项 生产量/件
腰兜 胸兜
1 直兜带盖 船型 5
2 斜兜带盖 船型 2
3 明贴兜 船型 2
4 直兜双牙 直型 3
5 明贴兜 明贴兜 1
6 明贴兜 直型 4
7 直兜带盖 船型 1
8 斜兜双牙 船型 2
9 斜兜双牙 船型 2
10 直兜双牙 直型 1
11 直兜双牙 船型 1
12 斜兜带盖 猎装口袋 1

Tab.2

Suit comprehensive process information table (front piece group)"

工
序		 定制
项		 定制
选项		 工艺 工时/
s		 紧前
工序
1 — — 缝合腰省 67.0 —
2 — — 烫腰省 34.5 1
3 — — 缝合前片与侧片 116.3 —
4 — — 归烫驳口 20 —
5 — — 拔烫肩线 18 3
6 — — 拔腰归臀 41.2 3
7 — — 缉缝袖窿牵条 79.5 2,4,5,6
8 腰
兜		 Y1,Y4 做腰兜兜盖 139 —
Y2,Y3,Y5 无 0
9 腰
兜		 Y1,Y4 扣烫嵌线,画兜位 45.4 2,4,5,6
Y2,Y5 扣烫嵌线,画兜位 42.4
Y3 画兜位 10.6
10 腰
兜		 Y1,Y4 缉缝固定嵌线和兜盖 6.9 8,9
Y2,Y5 无 0
Y3 修剪兜布缝份并锁边 14.7
11 腰
兜		 Y1,Y4 将嵌线、兜盖缉在衣片上 103.2 10
Y2,Y5 将嵌线缉在衣片上 103.2
Y3 兜口处粘衬并翻折熨烫 13.7
12 腰
兜		 Y1,Y2,
Y4,Y5		 剪开兜口、固定“三角” 73.7 11
Y3 无 0
13 腰
兜		 Y1,Y2,
Y4,Y5		 绱兜布、缉兜布 104.6 12
Y3 明线缉缝腰兜 57.7
14 — — 画胸兜兜位 3.7 —
15 胸
兜		 X1,X2 扣烫兜口、修剪兜垫 22.5 14
X3 修剪兜布缝份并锁边 14.7
X4 做贴袋 24.4
16 胸
兜		 X1,X2 粘胸兜兜板衬,扣烫缝份 18.7 —
X3 兜口处粘衬并翻折熨烫 13.7
X4 做兜盖 139.0
17 胸
兜		 X1,X2 缉缝胸兜兜板和兜垫 22.6 15,16
X3 明线缉缝胸兜并修烫 25.3
X4 装贴袋 57.7
18 胸
兜		 X1,X2 剪开兜口 15.4 17
X3 无 0
X4 装兜盖 6.9
19 胸
兜		 X1,X2 劈烫缝份 15.6 18
X3,X4 无 0
20 胸
兜		 X1,X2 缉缝固定胸兜兜板里 11.6 19
X3,X4 无 0
21 胸
兜		 X1,X2 固定胸兜兜板两端 67.9 20
X3,X4 无 0
22 胸
兜		 X1,X2 绱兜布 43.3 21
X3,X4 无 0
23 — — 缉胸衬上的省道 70.0 —
24 — — 纳衬 80.0 23
25 — — 烫衬 12.6 24
26 — — 绷缝胸衬与前衣片 18.4 7,22,25
27 — — 正面绷缝肩部与胸部 18.0 26
28 — — 绷缝胸兜布与胸衬 10.0 27
29 — — 正面绷缝胸衬外缘 15.0 28
30 — — 绷缝侧片缝份与胸衬 12.0 28
31 — — 敷牵条 98.5 29,30
32 — — 修剪胸衬肩部、质检 45.4 31

Tab.3

Trial optimization results(front group)"

工位
数		 工位包含最
大工序数		 实际生产
节拍/s		 平衡
指数		 目标
函数值		 是否
符合
13 5 102.94 9.61 65.61 否
14 4 97.75 16.54 65.27 是
15 4 94.65 18.15 64.05 是
16 4 94.65 25.37 66.94 是
17 4 86.25 45.86 70.09 是

Tab.4

Process planning (Front group)"

工位 工序 工位节拍/s
1 1、4、14 90.70
2 3-1、15 80.41
3 2、3-2 92.65
4 16、23 93.31
5 5、6、17、18 97.75
6 19、24 94.35
7 17、20 90.17
8 9、21 97.05
9 8、10、22 96.48
10 11、25 90.74
11 12、26、27 89.46
12 28、29、30、31-1 86.25
13 31-2、32 94.65
14 13 91.46

Tab.5

Summary of process preparation efficiency"

组别 工位数 工位平均节拍/s 工位瓶颈节拍/s 编排效率/%
前片 14 91.82 97.75 93.93
过面 8 89.85 102.40 87.74
后片 8 80.58 91.20 88.36
领子 3 76.72 89.73 85.50
袖子 7 93.52 115.68 80.84
组装 16 95.15 121.60 78.25
平均 — — — 85.77
[1] 黄辉, 李梦想, 严永. 考虑序列设置时间的混合流水车间多目标调度研究[J]. 运筹与管理, 2020, 29(12):215-221.
doi: 10.12005/orms.2020.0334
HUANG Hui, LI Mengxiang, YAN Yong. Research on multi-objective scheduling of hybrid flow shop considering sequence setting time[J]. Operations Research and Management, 2020, 29(12): 215-221.
[2] NIKJO B, ZAROOK Y. A non-permutation flow shop manufacturing cell scheduling problem with part's sequence dependent family setup times[J]. International Journal of Applied Metaheuristic Computing, 2017, 5(4):70-86.
doi: 10.4018/ijamc.2014100104
[3] 蒋增强, 左乐. 低碳策略下的多目标柔性作业车间调度[J]. 计算机集成制造系统, 2015, 21(4):1023-1031.
JIANG Zengqiang, ZUO Le. Multi-objective flexible job shop scheduling under low-carbon strategy[J]. Computer Integrated Manufacturing System, 2015, 21(4): 1023-1031.
[4] 顾泽平, 杨建军, 周勇. 不确定因素扰动下多目标柔性作业车间鲁棒调度方法[J]. 计算机集成制造系统, 2017, 23(1):66-74.
GU Zeping, YANG Jianjun, ZHOU Yong. Robust scheduling method for multi-objective flexible job shop under the disturbance of uncertain factors[J]. Computer Integrated Manufacturing System, 2017, 23(1):66-74.
[5] 韩煜东, 董双飞, 谭柏川. 基于改进遗传算法的混装线多目标优化[J]. 计算机集成制造系统, 2015, 21(6):10.
HAN Yudong, DONG Shuangfei, TAN Baichuan. Multi-objective optimization of mixed assembly line based on improved genetic algorithm[J]. Computer Integrated Manufacturing System, 2015, 21(6):10.
[6] 黄学文, 陈绍芬, 周阗玉, 等. 求解柔性作业车间调度的遗传算法综述[J]. 计算机集成制造系统, 2022, 28(2):16.
HUANG Xuewen, CHEN Shaofen, ZHOU Tianyu, et al. Overview of genetic algorithms for solving flexible job shop scheduling[J]. Computer Integrated Manufacturing System, 2022, 28(2):16.
[7] 戴敏, 王辉. 基于遗传退火算法的绿色作业车间调度[J]. 现代制造工程, 2021(5):26-31.
DAI Min, WANG Hui. Green job shop scheduling based on genetic annealing algorithm[J]. Modern Manufacturing Engineering, 2021(5):26-31.
[8] 黄珍珍, 莫碧贤, 温李红. 基于遗传算法及仿真技术的服装生产流水线平衡[J]. 纺织学报, 2020, 41(7):154-159.
HUANG Zhenzhen, MO Bixian, WEN Lihong. Balance of garment production line based on genetic algorithm and simulation technology[J]. Journal of Textile Research, 2020, 41(7):154-159.
[9] 唐浩, 黎向锋, 张丽萍, 等. 扰动机制下的遗传算法求解柔性作业车间调度[J]. 现代制造工程, 2021(7):1-9.
TANG Hao, LI Xiangfeng, ZHANG Liping, et al. Genetic algorithm under disturbance mechanism for flexible job shop scheduling[J]. Modern Manufacturing Engineering, 2021(7):1-9.
[10] 楚克明, 吴立云. 自适应遗传算法在混流装配线排序中的应用[J]. 现代制造工程, 2019(9):37-40.
CHU Keming, WU Liyun. Application of adaptive genetic algorithm in mixed-flow assembly line sequenc-ing[J]. Modern Manufacturing Engineering, 2019(9):37-40.
[11] 杨艳, 赵维强, 陶惠绢, 等. 遗传算法在男西裤生产流水线平衡中的应用[J]. 山东纺织科技, 2019, 60(1):28-31.
YANG Yan, ZHAO Weiqiang, TAO Huijuan, et al. Application of genetic algorithm in the balance of men's trousers production line[J]. Shandong Textile Science and Technology, 2019, 60(1):28-31.
[12] 张旭靖, 王立川, 陈雁. 基于遗传算法的服装缝制生产线平衡优化[J]. 纺织学报, 2020, 41(2):125-129.
ZHANG Xujing, WANG Lichuan, CHEN Yan. Balance optimization of garment sewing production line based on genetic algorithm[J]. Journal of Textile Research, 2020, 41(2):125-129.
[13] 梅自强. 纺织辞典[M]. 北京: 中国纺织出版社, 2007:755.
MEI Ziqiang. Textile dictionary[M]. Beijing: China Textile & Apparel Press, 2007:755.
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