Journal of Textile Research ›› 2024, Vol. 45 ›› Issue (08): 225-233.doi: 10.13475/j.fzxb.20230703801

• Machinery & Equipment • Previous Articles     Next Articles

Advanced planning and scheduling system and scheduling algorithm for intelligent warp knitting workshop

HUANG Chao1,2, ZHANG Jianming2,3(), CHEN Hao2,3, LIU Weiqi2, ZHANG Haoyu2, GUO Meng2   

  1. 1. College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou, Fujian 350100, China
    2. Fujian Institute of Research on the Structure, Chinese Academy of Sciences, Fuzhou, Fujian 350100, China
    3. Fujian Provincial Key Laboratory of Intelligent Identification and Control of Complex Dynamic System, Quanzhou, Fujian 362000, China
  • Received:2023-07-17 Revised:2024-01-16 Online:2024-08-15 Published:2024-08-21
  • Contact: ZHANG Jianming E-mail:zhangjianming@fjirsm.ac.cn

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Abstract:

Objective The textile industry gradually shifts towards multi-variety, small-batch, and order-based production, and the traditional production management models no longer meet the demands for flexible customization management in large-scale production environments. This issue is particularly prominent in warp knitting production enterprises with complex and diverse products. To address the issues of low efficiency and incomplete consideration factors in traditional manual scheduling in the warp knitting industry, this paper reports an advanced planning and scheduling (APS) for warp knitting, aiming to effectively improving production continuity and order delivery efficiency.

Method APS system based on microservices architecture for warp knitting workshops is proposed. After elaborating in detail the production planning and scheduling operation mechanism based on APS, a multi-objective warp knitting workshop scheduling model was constructed to minimize the maximum completion time and the number of raw material changes. An optimization algorithm based on Non-dominated Sorting Genetic Algorithm II (NSGAⅡ) is designed and implemented to solve the intelligent allocation problem for large-scale equipment and orders.

Results It is found that APS system can effectively make up for the shortcomings of traditional enterprise resource planning (ERP) and manufacturing execution system (MES) single production planning management mode, which is separated from actual production, lack of production planning and decision support. Through the integration with MES, ERP and other systems, comprehensive data analysis and mining, the development of detailed production plans, to provide decision support for production management. According to the actual production situation, the production process is dynamically adjusted and optimized. Experimental verification shows that the optimization effect of NSGA-II based warp knitting shop scheduling optimization method can reach more than 200% as the scale increases in terms of maximum completion time and the number of raw material changes between orders. Compared with the traditional multi-objective genetic algorithm, the scheduling results of this algorithm for small-scale problems are not much different. However, with the expansion of the problem scale, the optimization ability of the traditional multi-objective genetic algorithm decreases significantly, which may lead to longer optimization time, local optimal solution, loss of excellent properties, and even worse scheduling results than manual scheduling.

Conclusion This paper proposes a warp knitting production APS system based on microservice architecture according to actual production needs. It can meet the complex business processes of warp knitting production and future business expansion, and can monitor and coordinate the management of orders and resources in the warp knitting production process in real time, better meeting the actual needs of the warp knitting workshop. In addition, this paper also uses the NSGA-Ⅱ algorithm to optimize scheduling problems, which can significantly improve production efficiency and continuity, effectively solve the problem of effective allocation of large-scale orders and equipment, and can adapt to further expansion in the future. In summary, the warp knitting production APS system reported in this paper is an efficient and intelligent production management tool with a wide range of application prospects and promotional value.

Key words: micro service architecture, advanced planning and scheduling system, warp knitting shop, workshop scheduling optimization method, non-dominated sorting genetic algorithm, intelligent production

CLC Number: 

  • TP181

Fig.1

APS microservice architecture diagram"

Fig.2

Production planning and scheduling flow chart based on APS"

Fig.3

Warp knitting production process"

Tab.1

Warp knitting shop scheduling parameter"

调度参数 参数描述
i 订单编号,i=1,2,3,…,n
j 经编机编号,j=1,2,3,…,m
n 订单数量
m 经编机台数
u j 机台j上有u个订单
O j k 机台j上的第k个订单
w i j 订单i是否分配到机台j上,   w i j = 0或1
M O j k O j ( k - 1 ) 机台j上第k个订单与第 k - 1个订单需要更换原料的次数
L i 订单i的长度,单位为m
P i 订单i的生产速度,单位为m/min
S i 订单i在经编机j上加工时长,单位为min
C j 机台j加工总时长,单位为min
Q j 机台j更换原料次数

Fig.4

algorithm flow chart"

Fig.5

Chromosome coding and decoding diagram"

Tab.2

Comparison of different scale scheduling results"

调度规模 th/min tM/min tN/min nh nM nN
20×100 5 753.2±445.8 5 012.4±256.9 3 076.2±167.8 257±79.2 208±52.5 165±31.6
20×200 6 672.4±678.9 5 978.4±424.6 3 453.2±221.5 454±98.3 394±78.3 257±42.4
50×200 6 034.2±899.6 5 987.7±789.7 2 799.3±245.9 607±108.0 413±88.7 206±45.9
50×500 9 023.4±1 478.5 8 876.9±975.9 4 067.2±387.7 1 478±364.7 1 012±339.3 389±78.7
100×500 7 033.2±1 522.3 6 577.9±1 008.2 3 044.5±543.9 1 378±448.9 978±398.9 549±114.5
100×1 000 15 323.2±2 311.6 13 878.7±1 657.6 6 277.9±876.9 2 978±569.8 2 019±421.0 949±289.7
200×1 000 11 562.7±2 981.7 8 089.1±1 879.9 4 907.2±931.2 2 768±643.7 1 867±522.7 879±453.6
200×2 000 19 218.3±3 467.0 12 378.9±2 178.1 8 987±997.2 6 357±1 556.3 5 697±865.8 2 231±525.7
500×2 000 14 232.1±4 436.3 10 238.8±2 679.1 5 789±1 067.7 6 123±1 623.8 5 588±1 548.9 2 312±667.9
500×4 000 23 431.3±5 789.9 17 897.6±3 398.2 10 896±1 564.5 11 272±3 178.1 9 245±1 780.1 4 123±891.0

Tab.3

Different scale scheduling optimization rate %"

调度规模 R1 R2 R3 R4
20×100 47.0 33.9 55.7 26.1
20×200 63.2 45.1 76.6 53.1
50×200 77.6 60.8 105.3 67.5
50×500 107.9 89.2 158.8 98.4
100×500 131.0 116.1 178.5 112.7
100×1 000 164.3 121.1 236.1 167.5
200×1 000 175.8 155.8 267.4 189.4
200×2 000 196.6 179.3 356.9 243.8
500×2 000 221.4 205.2 389.3 267.1
500×4 000 289.1 268.0 466.1 289.2

Fig.6

Minimize maximum completion time optimal fitness variation"

Fig.7

Minimize optimal fitness change of number of raw material changes between orders"

Fig.8

Pareto frontier diagram"

Fig.9

Main function modules of APS system"

[1] 丁欢, 胡建, 李涵, 等. 新经济常态下纺织行业发展的思考[J]. 中国纤检, 2023, 570(3): 35-37.
DING Huan, HU Jian, LI Han, et al. Thinking on the development of textile industry under the new economic normal[J]. China Fiber Inspection, 2023, 570(3): 35-37.
[2] 纺织行业经济发展形势与“十四五”发展重点[J]. 纺织检测与标准, 2021, 7(1): 45-48.
Textile industry economic development situation and "fourteen five" development focus[J]. Textile Testing and Standards, 2021, 7(1): 45-48.
[3] 刘凯琳. 经编织物在产业用领域的发展及应用[J]. 纺织导报, 2022, 43(5): 27.
LIU Kailin. Development and application of warp-knitted technical textiles[J]. China Textile Leader, 2022, 43(5): 27.
[4] 张洁, 徐楚桥, 汪俊亮, 等. 数据驱动的机器人化纺织生产智能管控系统研究进展[J]. 纺织学报, 2022, 43(9): 1-10.
ZHANG Jie, XU Chuqiao, WANG Junliang, et al. Advancement in data-driven intelligent controlsystem for roboticized textile production[J]. Journal of Textile Research, 2022, 43(9): 1-10.
[5] 朱启, 蒋高明, 丛洪莲, 等. 基于B/S结构的经编MES系统[J]. 纺织学报, 2013, 34(1): 128-132.
ZHU Qi, JIANG Gaoming, CONG Honglian, et al. Development of manufacturing execution system of warp knitting based on B/S mode[J]. Journal of Textile Research, 2013, 34(1): 128-132.
[6] 邵景峰, 贺兴时, 王进富, 等. 大数据环境下的纺织制造执行系统设计[J]. 机械工程学报, 2015, 51(5): 160-170.
doi: 10.3901/JME.2015.05.160
SHAO Jingfeng, HE Xingshi, WANG Jinfu, et al. Design of textile manufacturing execution system based on big data[J]. Journal of Mechanical Engineering, 2015, 51(5): 160-170.
doi: 10.3901/JME.2015.05.160
[7] FACHINI R F, ESPOSTO K F, CAMARGO V C B. A framework for development of advanced planning and scheduling (APS) systems in glass container indu-stry[J]. Journal of Manufacturing Technology Manag-ement, 2018, 29(3): 570-587.
[8] 余建国, 木柏林. 面向机电制造企业的APS系统研究[J]. 机电工程技术, 2022, 51(9): 22-25.
YU Jianguo, MU Bolin. Research on APS system for electromechanical manufacturing enterprises[J]. Electrical Engineering Technology, 2022, 51(9): 22-25.
[9] SERRANO-RUIZ J C, MULA J, POLER R. Smart manufacturing scheduling: a literature review[J]. Journal of Manufacturing Systems, 2021, 61: 265-287.
[10] 卢颖涛. 针织企业染整车间调度方法研究[D]. 上海: 东华大学, 2019: 1-60.
LU Yingtao. Research on dyeing production scheduling in dyeing and finishing workshop of knitting com-pany[D]. Shanghai: Donghua University, 2019: 1-60.
[11] HUYNH N T, CHIEN C F. A hybrid multi-subpopulation genetic algorithm for textile batch dyeing scheduling and an empirical study[J]. Computers & Industrial Engineering, 2018, 125: 615-627.
[12] 蔡飞飞, 郗欣甫, 沈瑞超, 等. 经编车间过程监控与生产调度[J]. 东华大学学报(自然科学版), 2020, 46(6): 952-958.
CAI Feifei, CHI Xinfu, SHEN Ruichao, et al. Process monitoring and production scheduling in warp knitting workshop[J]. Journal of Donghua University (Natural Science), 2020, 46(6): 952-958.
[13] KIM J G, SONG S, JEONG B J. Minimising total tardiness for the identical parallel machine scheduling problem with splitting jobs and sequence-dependent setup times[J]. International Journal of Production Research, 2020, 58(6): 1628-1643.
[14] PANT M, SNASEL V, VERMA S. A comprehensive review on NSGA-II for multi-objective combinatorial optimization problems[J]. IEEE Access, 2024, 9: 57757-57791.
[15] BANDYOPADHYAY S, BHATTACHARYA R. Solving multi-objective parallel machine scheduling problem by a modified NSGA-II[J]. Applied Mathematical Modelling, 2013, 37(10/11): 6718-6729.
[16] WANG J A, PAN R, GAO W, et al. An automatic scheduling method for weaving enterprises based on genetic algorithm[J]. Journal of The Textile Institute, 2015, 106(12): 1377-1387.
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