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

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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
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