College of Art and Design, Shaanxi University of Science and Technology, Shaanxi, Xi'an, China.
College of Mechanical & Electrical Engineering, Shaanxi University of Science and Technology, Shaanxi, Xi'an, China.
Comput Intell Neurosci. 2022 Aug 21;2022:2293473. doi: 10.1155/2022/2293473. eCollection 2022.
In order to improve the efficiency of cloth laying and cutting integrated production process, this article proposes a method of optimal scheduling of cloth laying and cutting garment production system process based on big data and genetic algorithm. The chromosomes in the algorithm are expressed by real strings. The method of bit string crossover and mutation is used to solve the premature problem of the algorithm. The experimental results show that the actual cutting time of the plan is 736 min, and the total idle time is 113 min. The idle time occurs in processes 25, 28, 34, 35, and 31, respectively. The cutting time of the plan arranged by the genetic algorithm is 627 min, and there is no idle time. . This method can effectively solve the optimal scheduling problem of the cloth laying and cutting production process.
为提高布料铺放和裁剪集成生产过程的效率，本文提出了一种基于大数据和遗传算法的服装生产系统布料铺放和裁剪的优化调度方法。算法中的染色体采用实数值字符串表示，采用位字符串交叉和变异方法解决算法早熟问题。实验结果表明，该方案的实际切割时间为 736 分钟，总空闲时间为 113 分钟。空闲时间分别发生在过程 25、28、34、35 和 31。遗传算法安排的计划的切割时间为 627 分钟，没有空闲时间。这种方法可以有效地解决布料铺放和裁剪生产过程的优化调度问题。
