纺织学报 ›› 2020, Vol. 41 ›› Issue (06): 36-41.doi: 10.13475/j.fzxb.20181107906

• 纺织工程 • 上一篇    下一篇

基于模拟退火遗传算法的纺纱车间调度系统

郑小虎(), 鲍劲松, 马清文, 周衡, 张良山   

  1. 东华大学 机械工程学院, 上海 201620
  • 收稿日期:2018-11-28 修回日期:2020-03-15 出版日期:2020-06-15 发布日期:2020-06-28
  • 作者简介:郑小虎(1983—),男,副教授,博士。主要研究方向为智能制造技术。E-mail:xhzheng@dhu.edu.cn
  • 基金资助:
    国家重点研发计划项目(2017YFB1304000);上海市经信委项目(2018-RG2N-02055)

Spinning workshop collaborative scheduling method based on simulated annealing genetic algorithm

ZHENG Xiaohu(), BAO Jinsong, MA Qingwen, ZHOU Heng, ZHANG Liangshan   

  1. College of Mechanical Engineering, Donghua University, Shanghai 201620, China
  • Received:2018-11-28 Revised:2020-03-15 Online:2020-06-15 Published:2020-06-28

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摘要：

为解决有自动引导运输车(AGV)的环锭纺纱车间协同调度系统多种约束条件下的调度问题,在考虑工艺、加工设备资源、AGV资源以及批处理4种约束条件的情况下,建立了满足最大完工时间最小化和设备利用率最大化的AGV纺纱车间协同调度模型。针对模拟退火和遗传算法计算效率低和易陷入局部最优解的缺点,提出了基于模拟退火遗传算法的纺纱车间调度模型求解算法。实验结果表明:当给定条筒为50个时,同等环境下,基于模拟退火遗传算法的调度方案要比普通的模拟退火和遗传算法的最大完工时间分别减少了1 162 s和 1 619 s,纺纱车间的设备和AGV的利用率也分别提高了将近12%和11%。该方法在提升环锭纺纱车间运行效率方面具有一定的应用价值。

关键词: 模拟退火遗传算法, 多目标优化, 自动引导运输车, 纺纱车间, 协同调度

Abstract:

In order to solve the multi-objective scheduling problem of automated guided vehicle(AGV) spinning workshop collaborative scheduling system, under the four constraints of technology, processing equipment resources, AGV resources, and batch processing, an AGV spinning workshop collaborative scheduling system model that meets the minimum completion time and maximizes equipment utilization was established. Then, based on the shortcomings of simulated annealing and genetic algorithm, such as low efficiency and easy to fall into local optimal solution, a spinning scheduling system based on simulated annealing genetic algorithm was proposed. The results show that when the number of cotton drums is 50, the scheduling scheme based on simulated annealing genetic algorithm is reduced by 1 162 s and 1 619 s respectively than the simulated annealing and genetic algorithm in the same environment. The utilization rate of equipment and AGV in the yarn workshop has also increased by nearly 12% and 11% respectively. This method has application value in improving the operation efficiency of the ring spinning workshop.

Key words: simulated annealing genetic algorithm, multi-objective optimization, automated guided vehicle, spinning workshop, collaborative scheduling

中图分类号: 

  • TS112.7

图1

环锭纺生产工艺流程"

图2

模拟退火遗传算法流程"

表1

设备利用率"

条筒
个数		 批处理机设备利用率 AGV设备利用率
SA GA SA-GA SA GA SA-GA
5 43 46 59 53 57 68
10 43 48 60 54 55 69
15 44 52 61 56 58 67
20 42 56 63 58 61 73
25 45 53 62 53 55 65
30 48 52 61 55 57 68
35 48 52 64 57 59 72
40 55 54 63 52 56 66
45 42 51 58 50 55 67
50 52 50 64 52 58 69

表2

最大完工时间与算法处理时间"

条筒
个数		 最大完工时间 算法计算时间
SA GA SA-GA SA GA SA-GA
5 1 750 2 153 1 745 11 13 12
10 2 966 3 340 2 879 26 25 26
15 4 861 5 013 3 792 35 42 40
20 6 545 6 857 4 905 48 56 55
25 7 782 8 285 6 694 60 78 81
30 8 921 9 351 7 684 74 92 88
35 9 937 10 416 8 790 97 103 104
40 10 853 11 272 9 699 108 117 115
45 11 792 12 383 10 748 124 128 126
50 12 849 13 306 11 687 143 155 138
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