Journal of Textile Research ›› 2022, Vol. 43 ›› Issue (05): 156-162.doi: 10.13475/j.fzxb.20210504207

• Apparel Engineering • Previous Articles     Next Articles

Optimization modeling for packaging order allocation for garment enterprises and solution finding using non-dominated genetic algorithm

PAN Jiahao1,2, ZHOU Qihong1,2(), CEN Junhao3, LI Shujia1, ZHOU Shenhua1   

  1. 1. College of Mechanical Engineering, Donghua University, Shanghai 201620, China
    2. Engineering Research Center of Digitalized Textile and Fashion Technology, Ministry of Education, Donghua University, Shanghai 201620, China
    3. Guangzhou Seyounth Automation Technology Co., Ltd., Guangzhou, Guangdong 511400, China
  • Received:2021-05-18 Revised:2022-01-07 Online:2022-05-15 Published:2022-05-30
  • Contact: ZHOU Qihong E-mail:zhouqihong@dhu.edu.cn

RichHTML

3

PDF (PC)

79

Abstract:

In order to solve the problems of irrational task allocation scheme and long delay time in order allocation and sorting in the packaging and delivery process of garment manufacturers, a two-objective optimization mathematical model for packaging order allocation and sorting with maximum completion time and tardiness as objective functions was established taking into account for the constraints to machine allocation and machine adjustment time of the packaging line. A reference-point-based multi-objective evolutionary algorithm was used to solve the model before decoding the optimized solution and output the optimized scheme. This model was employed to solve a specific problem encountered by an enterprise, the results demonstrate that the new model is able to shorten the maximum completion time and can effectively control the delay. The average maximum completion time of several optimization schemes is 4.7% shorter than that of the earliest due date rule schemes, and the total delay time of all schemes is less than 4 hours. The research findings are of good application value in improving the packaging and shipping efficiency of garment manufacturers.

Key words: packaging order, allocation and sorting, multi-objective optimization, non-dominated sorting, genetic algorithm, garment enterprise

CLC Number: 

  • TS941

Fig.1

NSGA-Ⅲ algorithm flow chart"

Fig.2

Individual encoding and sort decoding"

Fig.3

Example and effects of single-point PMX crossover"

Fig.4

Example of mutation"

Tab.1

Data of model validation examples"

订单号 订单种类 交货期/h 箱数/围巾数 处理时间/h
装箱类型1 装箱类型2 装箱类型3 装箱类型4 装箱类型5
1 1* 65 525/18 900 44.187 5
2 1* 72 475/17 100 62/1 116 360/8 640 63.417 5
3 2* 60 14/1 190 2.702 8
4 2* 78 9/198 0.477 5
5 2* 67 22/3 300 7.425
6 2* 58 10/500 2/80 2/60 1.480 6
7 2* 74 5/475 1/71 1.230 3
8 1* 69 20/280 0.705 6
9 3* 85 459/10 016 17.212 5
10 3* 77 62/2 232 5.218 3
11 2* 70 11/1 320 1/48 5/650 1/118 4.821 7
12 2* 62 7/840 1/88 4/520 1/32 3.343 1
13 1* 88 177/4 248 10.177 5
14 3* 96 176/6 336 14.813 3
15 2* 94 53/1 166 1/15 2/38 127/2 032 1/10 8.013 3

Tab.2

Assignment sorting scheme according to EDD rule"

分配与排序方案 拖期情况 最大完成
时间/h		 总拖
期/h
[6,12,5,8,11,2,15] 0,0,0,0,0,11.393 5,0 99.919 7 15.313 2
[3,1,7,10,4,9,13,14] 0,0,0,0,0,0,0,3.919 7

Fig.5

Iteration diagram of maximum completion time and total delay. (a) Maximan completion time; (b) Total delay"

Fig.6

Integrated fitness iteration diagram"

Fig.7

Values distribution of two objective functions of optimized population"

Tab.3

Schemes corresponding to optimized results"

方案序号 分配方案 排序方案 拖期订单 拖期时间/h 最大完成时间/h 总拖期/h
1 [6,7,11,3,8,2,13,15] [4,5,12,1,10,9,14] 2号订单
15号订单		 3.458 5
0.149 3		 94.177 2 3.607 8
2 [6,3,7,11,4,2,13,15] [5,12,1,8,10,9,14] 2号订单 3.230 4 94.405 3 3.230 4
3 [7,6,3,11,2,13,15] [4,5,12,1,8,10,9,14] 2号订单 2.752 9 94.882 8 2.752 9
4 [7,6,3,11,2,13,15] [11,5,4,1,10,9,14] 2号订单 1.979 9 95.655 8 1.979 9
5 [6,3,7,4,12,2,13,15] [11,5,1,8,10,9,14] 2号订单 1.751 8 95.883 9 1.751 8
6 [7,6,3,12,2,13,15] [11,4,5,1,8,10,9,14] 2号订单
14号订单		 1.274 3
0.361 4		 96.361 4 1.635 7
[1] 夏明. 智能制造在纺织服装工业的应用现状与展望[J]. 中国纺织, 2019(11):168-169.
XIA Ming. The application status and prospect of intelligent manufacturing in textile and garment industry[J]. China Textile, 2019(11):168-169.
[2] 闫玺铃. 面向订单的混流生产线车间调度模型的研究[D]. 太原: 中北大学, 2020:12-14.
YAN Xiling. Research on shop scheduling model of order-oriented mixed flow production line[D]. Taiyuan: North University of China, 2020:12-14.
[3] BEKTUR G, SARAC T. A mathematical model and heuristic algorithms for an unrelated parallel machine scheduling problem with sequence-dependent setup times, machine eligibility restrictions and a common server[J]. Computers & Operations Research, 2019, 103: 46-63.
doi: 10.1016/j.cor.2018.10.010
[4] 黄敏芳, 张源凯, 王颜新, 等. 基于JIT装配模式的网上超市订单分拣优化模型[J]. 中国管理科学, 2020, 28(5): 159-166.
HUANG Minfang, ZHANG Yuankai, WANG Yanxin, et al. Online supermarket order sorting optimization model based on JIT assembly model[J]. Chinese Journal of Management Science, 2020, 28(5): 159-166.
[5] 郑小虎, 鲍劲松, 马清文, 等. 基于模拟退火遗传算法的纺纱车间调度系统[J]. 纺织学报, 2020, 41(6):36-41.
ZHENG Xiaohu, BAO Jinsong, MA Qingwen, et al. Spinning workshop collaborative scheduling method based on simulated annealing genetic algorithm[J]. Journal of Textile Research, 2020, 41(6): 36-41.
[6] 孙玉姣, 胡祥培, 曾庆成. 集装箱码头装卸作业顺序多目标优化调度模型[J]. 系统工程理论与实践, 2020, 40(1):195-209.
doi: 10.12011/1000-6788-2018-1000-15
SUN Yujiao, HU Xiangpei, ZENG Qingcheng. A multi-objective optimization model for operation scheduling in container terminals[J]. Systems Engineering-Theory & Practice, 2020, 40(1):195-209.
[7] 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.
HUANG Hui, LI Mengxiang, YAN Yong. Research on multi-objective scheduling of hybrid flow production shop considering sequence setting time[J]. Operations Research and Management Science, 2020, 29(12):215-221.
[8] 裴小兵, 杨景霞. 一种解决带有紧急插单问题的果蝇优化算法[J]. 系统工程, 2020, 38(6):139-146.
PEI Xiaobing, YANG Jingxia. A fruit fly optimization algorithm for solving the problem with emergent insertion[J]. Systems Engineering, 2020, 38(6):139-146.
[9] 王家海, 李营力, 刘铮玮, 等. 柔性作业车间调度的精确邻域结构混合进化算法[J]. 同济大学学报(自然科学版), 2021, 49(3):440-448.
WANG Jiahai, LI Yingli, LIU Zhengwei, et al. Evolutionary algorithm with precise neighborhood structure for flexible workshop scheduling[J]. Journal of Tongji Univer-sity(Natural Science Edition), 2021, 49(3):440-448.
[10] 李俚, 谭大燕. 成品出货集装箱装箱作业均衡研究[J]. 装备制造技术, 2020(6):158-161.
LI Li, TAN Dayan. Study on the balance of container loading operation for finished product shipment[J]. Equipment Manufacturing Technology, 2020(6):158-161.
[11] PRATA B D A, RODRIGUES C D, FRAMINAN J M. Customer order scheduling problem to minimize makespan with sequence-dependent setup times[J]. Computers & Industrial Engineering, 2021, 151: 106962.
doi: 10.1016/j.cie.2020.106962
[12] LAHA D, GUPTA J N D. An improved cuckoo search algorithm for scheduling jobs on identical parallel machines[J]. Computers & Industrial Engineering, 2018, 126: 348-360.
doi: 10.1016/j.cie.2018.09.016
[13] EWEES A A, AL-QANESS M A A, ABD ELAZIZ M. Enhanced salp swarm algorithm based on firefly algorithm for unrelated parallel machine scheduling with setup times[J]. Applied Mathematical Modelling, 2021, 94: 285-305.
doi: 10.1016/j.apm.2021.01.017
[14] DEB K, JAIN H. An Evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach: part I: solving problems with box constraints[J]. IEEE Transactions on Evolutionary Computation, 2014, 18(4): 577-601.
doi: 10.1109/TEVC.2013.2281535
[1] SHEN Chunya, LEI Junjie, RU Xin, PENG Laihu, HU Xudong. Multi-objective large-scale dynamic scheduling for weaving workshops based on improved NSGAII [J]. Journal of Textile Research, 2022, 43(04): 74-83.
[2] SHAO Jingfeng, SHI Xiaomin. Multi-objective optimization of spinning process parameters based on nondominated sorting genetic algorithm II [J]. Journal of Textile Research, 2022, 43(01): 80-88.
[3] XIE Ziang, DU Jinsong, YU Yayun, CHEN Qingting, FEI Zhonghua. Optimization of components screening for hanging production line in suit mass customization [J]. Journal of Textile Research, 2021, 42(10): 150-156.
[4] XU Xuemei. Improved genetic algorithm for fabric formulation prediction based on simulated annealing algorithm [J]. Journal of Textile Research, 2021, 42(07): 123-128.
[5] ZHOU Yaqin, WANG Pan, ZHANG Peng, ZHANG Jie. Research on production scheduling method for weft knitting workshops [J]. Journal of Textile Research, 2021, 42(04): 170-176.
[6] ZHANG Zhuo, CONG Honglian, JIANG Gaoming, DONG Zhijia. Polo shirt rapid style recommendation system based on interactive genetic algorithm [J]. Journal of Textile Research, 2021, 42(01): 138-144.
[7] LI Liang, NI Junfang. Automatic generation algorithm for pattern processing codes of quilting machines [J]. Journal of Textile Research, 2020, 41(11): 162-167.
[8] XIE Ziang, DU Jinsong, ZHAO Guohua. Adaptive dynamic scheduling of garment hanging production line [J]. Journal of Textile Research, 2020, 41(10): 144-149.
[9] ZHANG Xiaoxia, LIU Fengkun, MAI Wei, MA Chongqi. Prediction of loom efficiency based on BP neural network and its improved algorithm [J]. Journal of Textile Research, 2020, 41(08): 121-127.
[10] HUANG Zhenzhen, MOK Pikyin, WEN Lihong. Garment production line balance based on genetic algorithm and simulation [J]. Journal of Textile Research, 2020, 41(07): 154-159.
[11] ZHENG Xiaohu, BAO Jinsong, MA Qingwen, ZHOU Heng, ZHANG Liangshan. Spinning workshop collaborative scheduling method based on simulated annealing genetic algorithm [J]. Journal of Textile Research, 2020, 41(06): 36-41.
[12] MO Shuai, FENG Zhanyong, TANG Wenjie, DANG Heyu, ZOU Zhenxing. Performance optimization of elastic spindle pipe based on neural network and genetic algorithm [J]. Journal of Textile Research, 2020, 41(04): 161-166.
[13] HUANG Qi, ZHOU Qihong, ZHANG Qian, WANG Shaozong, FAN Wei, SUN Huifeng. Layout optimization of dip dyeing workshop based on system layout planning-genetic algorithm [J]. Journal of Textile Research, 2020, 41(03): 84-90.
[14] ZHANG Xujing, WANG Lichuan, CHEN Yan. Balancing optimization of garment sewing assembly line based on genetic algorithm [J]. Journal of Textile Research, 2020, 41(02): 125-129.
[15] WANG Xiaohui, LIU Yuegang, MENG Zhuo, SUN Yize. Optimization of process parameters for 3D additive screen printing based on genetic algorithm and neural network [J]. Journal of Textile Research, 2019, 40(11): 168-174.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
京ICP备14006648号
Copyright 2021 © Editorial office of Journal of Textile Research
Supported by Beijing Magtech Co.Ltd