采用主目标进化遗传算法的织造排程研究

doi:10.13475/j.fzxb.20180505606

纺织学报 ›› 2019, Vol. 40 ›› Issue (8): 169-174.doi: 10.13475/j.fzxb.20180505606

采用主目标进化遗传算法的织造排程研究

孟朔¹, 潘如如¹(), 高卫东¹, 王静安¹, 周利军²

1.生态纺织教育部重点实验室(江南大学), 江苏无锡 214122
2.丹阳市丹盛纺织有限公司, 江苏镇江 212309

收稿日期:2018-05-23 修回日期:2019-05-13 出版日期:2019-08-15 发布日期:2019-08-16
通讯作者: 潘如如
作者简介:孟朔(1996—),男,硕士生。主要研究方向为纺织智能生产管理。
基金资助:
国家重点研发计划项目(2017YFB0309200);中央高校基本科研业务费专项资金项目(JUSRP51907A)

Research on weaving scheduling using main objective evolutionary genetic algorithm

MENG Shuo¹, PAN Ruru¹(), GAO Weidong¹, WANG Jing'an¹, ZHOU Lijun²

1. Key Laboratory of Eco-Textiles (Jiangnan University), Ministry of Education, Wuxi, Jiangsu 214122, China
2. Danyang Dansheng Textile Co., Ltd., Zhenjiang, Jiangsu 212309, China

Received:2018-05-23 Revised:2019-05-13 Online:2019-08-15 Published:2019-08-16
Contact: PAN Ruru

摘要/Abstract

摘要：

针对目前织造企业在订单多、批量小、品种杂、交期严格的情况下,织造排程效果差的问题,提出一种采用主目标进化遗传算法的织造排程方法。通过建立一个由织机类型约束的并行织造排程模型,以满足订单交期为主目标,最小化品种翻改、完工时间等为副目标,改进了普通快速非支配遗传算法,并以某织造车间的生产实例进行仿真实验。结果表明,此主目标进化算法排程效果优于企业人工排程方法,能够指导实际生产。为提高算法的鲁棒性,利用自适应调整遗传参数对算法优化,进一步提高质量,且在一定程度上避免了早熟,增强全局搜索能力。

关键词: 织造, 生产排程, 多目标优化, 非支配遗传算法

Abstract:

In order to solve the problems of large quantities, small specifications of orders, numerous products and strict delivery requirements in the textile industry, an automatic weaving scheduling method using main evolutionary genetic algorithm was proposed. A parallel weaving scheduling model with different loom type constraints was established to modify a common fast non-dominant genetic algorithm with the main goal of meeting the order delivery date and secondary goal of minimizing the change of variety and minimizing the completion time. Then a simulation experiment of the scheduling scheme was carried out. The result show that the effect of each objective function superior to the manual scheduling method, which shows a certain practical application effect to solve the current weaving scheduling problem. In order to further improve the performance of the algorithm, an adaptive method was carried out to optimize the genetic parameters. The optimized algorithm can avoid the precocity and enhance the global search ability, and the objective function of optimized algorithm is further improved.

Key words: weaving, production scheduling, multi-objective optimization, non-dominant genetic algorithm

中图分类号:

TS108.8

孟朔, 潘如如, 高卫东, 王静安, 周利军. 采用主目标进化遗传算法的织造排程研究[J]. 纺织学报, 2019, 40(8): 169-174.

MENG Shuo, PAN Ruru, GAO Weidong, WANG Jing'an, ZHOU Lijun. Research on weaving scheduling using main objective evolutionary genetic algorithm[J]. Journal of Textile Research, 2019, 40(8): 169-174.

图/表 7

表1

图1

图2

图3

图4

图5

图6

参考文献 10

[1]	ABEDINNIA H, GLOCK C H, SCHNEIDER M , et al. Machine scheduling problems in production: a tertiary study[J]. Computers & Industrial Engineering, 2017; 111:403-416.
[2]	邹泽桦, 曾九孙, 蔡晋辉 . 改进遗传算法求解柔性作业车间调度问题[J]. 计算机测量与控制, 2017,25(4):167-171.
	ZOU Zehua, ZENG Jiusun, CAI Jinhui . A modified genetic algorithm to solve the flexible job shop scheduling problem[J]. Computer Measurement & Control, 2017,25(4):167-171.
[3]	WANG Jing'an, PAN Ruru, GAO Weidong , et al. An automatic scheduling method for weaving enterprises based on genetic algorithm[J]. Journal of The Textile Institute, 2015,106(12):1377-1387. doi: 10.1080/00405000.2014.995463
[4]	韩江洪, 杜兆芳, 刘小平 , 等. 基于混合Petri网建模的纺织生产调度研究[J]. 系统仿真学报, 2008(24):6839-6842.
	HAN Jianghong, DU Zhaofang, LIU Xiaoping , et al. Research on textile production scheduling problems based on hybrid Petri net[J]. Journal of System Simulation, 2008(24):6839-6842.
[5]	EROGLU D Y, OZMUTLU H C . Solution method for a large-scale loom scheduling problem with machine eligibility and splitting property[J]. Journal of The Textile Institute, 2017,108(12):1-12. doi: 10.1080/00405000.2015.1133105
[6]	陈建能, 赵雄, 张国凤 , 等. 基于改进NSGA-Π算法的新型引纬机构的参数优化[J]. 纺织学报, 2008,29(1):110-113.
	CHEN Jianneng, ZHAO Xiong, ZHANG Guofeng , et al. Parameter optimization of a new weft insertion mechanism based on improved NSGA-Π[J]. Journal of Textile Research, 2008,29(1):110-113.
[7]	江君莉, 潘丰 . 改进蚁群算法求解多目标优化问题[J]. 服装学报, 2013(4):394-398.
	JIANG Junli, PAN Feng . Improved ant colony algorithm for multi-objective optimization problem[J]. Journal of Clothing Research, 2013(4):394-398.
[8]	DEB K, PRATAP A, AGARWAL S , et al. A fast and elitist multi-objective genetic algorithm:NSGA-II[J]. IEEE Transactions Evolutionary Computation, 2002,6(2):182-197. doi: 10.1109/4235.996017
[9]	蒋腾旭, 谢枫 . 遗传算法中防止早熟收敛的几种措施[J]. 计算机与现代化, 2006(12):54-56.
	JIANG Tengxu, XIE Feng . Several measures to prevent premature convergence in genetic algorithm[J]. Computer and Modernization, 2006(12):54-56.
[10]	黎钧琪, 石国桢 . 遗传算法交叉率与变异率关系的研究[J]. 武汉理工大学学报(交通科学与工程版), 2003,27(1):97-99.
	LI Junqi, SHI Guozhen . Study on the relationship between genetic algorithm crossover rate and variation rate[J]. Journal of Wuhan University of Techno-logy(Transportation Science Engineering Edition), 2003,27(1):97-99.

相关文章 15

[1]	王秋萍, 张瑞萍, 李成红, 张葛成. 导电涤纶非织造布的制备及其性能[J]. 纺织学报, 2020, 41(10): 116-121.
[2]	孙焕惟, 张恒, 甄琪, 朱斐超, 钱晓明, 崔景强, 张一风. 丙烯基纳微米弹性过滤材料的熔喷成型及其过滤性能[J]. 纺织学报, 2020, 41(10): 20-28.
[3]	谢子昂, 杜劲松, 赵国华. 衬衫吊挂流水线的自适应动态调度[J]. 纺织学报, 2020, 41(10): 144-149.
[4]	张凌云, 钱晓明, 邹驰, 邹志伟. SiO₂气凝胶/ 聚酯-聚乙烯双组分纤维复合保暖材料的制备及其性能[J]. 纺织学报, 2020, 41(08): 22-26.
[5]	陈诗萍, 陈旻, 魏岑, 王富军, 王璐. 医用防护服的构效特点及其研发趋势[J]. 纺织学报, 2020, 41(08): 179-187.
[6]	夏磊, 程博闻, 西鹏, 庄旭品, 赵义侠, 刘亚, 康卫民, 任元林. 闪蒸纺纳微米纤维非织造技术的研究进展[J]. 纺织学报, 2020, 41(08): 166-171.
[7]	安琪, 付译鋆, 张瑜, 张伟, 王璐, 李大伟. 医用防护服用非织造材料的研究进展[J]. 纺织学报, 2020, 41(08): 188-196.
[8]	周惠林, 杨卫民, 李好义. 医用口罩过滤材料的研究进展[J]. 纺织学报, 2020, 41(08): 158-165.
[9]	刘宜胜, 徐光逸. 斜吹气流入射角对纱线折入的影响[J]. 纺织学报, 2020, 41(07): 72-77.
[10]	戴鑫, 李晶, 陈晨. 镀铜碳纤维丝束细观耐磨性的有限元仿真模拟[J]. 纺织学报, 2020, 41(06): 27-35.
[11]	郑小虎, 鲍劲松, 马清文, 周衡, 张良山. 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(06): 36-41.
[12]	吕汉明, 王翔宇, 刘凤坤. 基于介电谱的醋酸酯水刺非织造布含水率估算[J]. 纺织学报, 2020, 41(06): 55-60.
[13]	莫帅, 冯战勇, 唐文杰, 党合玉, 邹振兴. 基于神经网络和遗传算法的锭子弹性管性能优化[J]. 纺织学报, 2020, 41(04): 161-166.
[14]	张星, 刘金鑫, 张海峰, 王玉晓, 靳向煜. 防护口罩用非织造滤料的制备技术与研究现状[J]. 纺织学报, 2020, 41(03): 168-174.
[15]	甄琪, 张恒, 朱斐超, 史建宏, 刘雍, 张一风. 聚丙烯/ 聚酯双组分微纳米纤维熔喷非织造材料制备及其性能[J]. 纺织学报, 2020, 41(02): 26-32.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

织机类型	高速品种	普通品种	复杂品种
多臂织机	可以织造	较为合适	最合适
踏盘织机	较为合适	最合适	不可织造
电子织机	最合适	不可织造	不可织造

采用主目标进化遗传算法的织造排程研究

Research on weaving scheduling using main objective evolutionary genetic algorithm

RichHTML

PDF (PC)

摘要/Abstract

引用本文

使用本文

图/表 7

参考文献 10

相关文章 15

Metrics

本文评价

推荐阅读 10