%0 Journal Article
%T 基于遗传算法的方形件订单组批与排样优化<br>Optimization of Order Batching and Layout of Square Parts Based on Genetic Algorithm
%A 朱锐
%A 李蒙
%A 秦恩广
%J Modeling and Simulation
%P 974-984
%@ 2324-870X
%D 2023
%I Hans Publishing
%R 10.12677/MOS.2023.122092
%X 由于订单组批和排样优化在个性化定制生产模式中至关重要，基于此，本文以减少原片板材用量为目标，构建了混合整数规划模型。通过编写算法来求解此模型，初始算法采用K-means聚类算法与遗传算法进行混合使用，通过K-means聚类算法将相似的item分类，然后以类别进行随机，建立遗传算法的初始种群，从而提高初始种群的适应度，在算法过程中按照指定规则按照染色体基因顺序放置，计算原片板材用量用以评估染色体适应度。结果如下：平均板材利用率为63%，平均板材使用数量为133块。之后提出贪婪策略优化算法，优化后的结果如下：平均板材利用率为85%，平均板材使用数量为97块。<br />
Since order grouping and scheduling optimization is crucial in the personalized production model, based on this, this paper constructs a mixed integer programming model with the goal of reducing the amount of original sheet metal used. The initial algorithm uses a mixture of K-means clustering algorithm and genetic algorithm to classify similar items by K-means clustering algorithm and then randomize them by category to establish the initial population of the genetic algorithm, thus im-proving the fitness of the initial population, and in the process of the algorithm, the chromosome genes are placed in order according to specified rules to calculate the original sheet. The plate usage was used to evaluate the chromosome fitness. The results are as follows: the average plate utiliza-tion is 63% and the average number of plates used is 133. After that, the greedy strategy optimiza-tion algorithm was proposed, and the optimized results were as follows: the average plate utiliza-tion was 85% and the average number of plates used was 97.
%K 订单组批，排样优化，K-Means算法，遗传算法；Order Group Batch
%K Scheduling Optimization
%K K-Means Algorithm
%K Genetic Algorithm
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=62396