经编车间高级计划排程系统及调度算法

doi:10.13475/j.fzxb.20230703801

纺织学报 ›› 2024, Vol. 45 ›› Issue (08): 225-233.doi: 10.13475/j.fzxb.20230703801

经编车间高级计划排程系统及调度算法

黄超¹^,², 张剑铭²^,³(), 陈豪²^,³, 刘维琦², 张浩宇², 郭萌²

1.福建农林大学机电工程学院, 福建福州 350100
2.中国科学院福建物质结构研究所, 福建福州 350100
3.福建省复杂动态系统智能辨识与控制重点实验室, 福建泉州 362000

收稿日期:2023-07-17 修回日期:2024-01-16 出版日期:2024-08-15 发布日期:2024-08-21
通讯作者: 张剑铭(1990—),男,工程师,博士。主要研究方向为生产数字化管理及车间调度优化方法。E-mail:zhangjianming@fjirsm.ac.cn。
作者简介:黄超(1998—),男,硕士生。主要研究方向为智能制造。
基金资助:
福建省科技计划项目-STS计划配套项目(2022T3010)

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. 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 Published:2024-08-15 Online:2024-08-21

摘要/Abstract

摘要：

多品种、小批量、订单化生产已成为纺织行业新常态,为解决现有管理模式与大规模柔性定制管理之间的矛盾,提出基于微服务架构的高级计划与排程(APS)系统,建立APS系统架构体系和功能模块,并基于APS系统的生产计划与调度运行机制,构建考虑最大完工时间和原料更换次数的多目标经编车间调度模型,采用非支配排序遗传算法(NSGA-Ⅱ)加以优化。结果表明:提出的APS系统能够有效地提高经编车间的生产效率,降低生产成本,缩短生产周期和交货期,满足客户需求和市场变化,为纺织生产企业的数字化升级提供了一种可行的解决方案。

关键词: 微服务架构, 高级计划排程系统, 经编车间, 车间调度优化方法, 非支配排序遗传算法, 智能生产

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

中图分类号:

TP181

黄超, 张剑铭, 陈豪, 刘维琦, 张浩宇, 郭萌. 经编车间高级计划排程系统及调度算法[J]. 纺织学报, 2024, 45(08): 225-233.

HUANG Chao, ZHANG Jianming, CHEN Hao, LIU Weiqi, ZHANG Haoyu, GUO Meng. Advanced planning and scheduling system and scheduling algorithm for intelligent warp knitting workshop[J]. Journal of Textile Research, 2024, 45(08): 225-233.

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链接本文: http://www.fzxb.org.cn/CN/10.13475/j.fzxb.20230703801

http://www.fzxb.org.cn/CN/Y2024/V45/I08/225

图/表 12

图1

图2

图3

表1

经编车间调度参数表"

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

表1

图4

图5

表2

表3

图6

图7

图8

图9

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调度规模	t_h/min	t_M/min	t_N/min	n_h	n_M	n_N
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

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

经编车间高级计划排程系统及调度算法

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

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