基于机器学习的服装生产线员工效率预测  

Employee efficiency prediction of garment production line based on machine learning

作　　者：鞠宇 王朝晖[1,2,3] 李博一[1] 叶勤文 JU Yu;WANG Zhaohui;LI Boyi;YE Qinwen(College of Fashion and Design,Donghua University,Shanghai 200051,China;Key Laboratory of Clothing Design&Technology,Ministry of Education,Donghua University,Shanghai 200051,China;Shanghai Belt and Road Joint Laboratory of Textile Intelligent Manufacturing,Shanghai 200051,China)

机构地区：[1]东华大学服装与艺术设计学院,上海200051 [2]现代服装设计与技术教育部重点实验室,上海200051 [3]上海市纺织智能制造与工程一带一路国际联合实验室,上海200051

出　　处：《纺织学报》2024年第5期183-192,共10页Journal of Textile Research

基　　金：上海市科学技术委员会“科技创新行动计划”“一带一路”国际合作项目(21130750100)。

摘　　要：在服装生产线中,管理者通常凭借直觉和经验进行工人调度和工序编排,缺少基于历史生产相关数据的分析,难以进行产前预判。为此,充分利用历史生产数据,使用机器学习技术科学地预判工人产前效率,以提高生产线的平衡率。首先,收集了某工厂13个订单的526个生产数据并通过分位数划分法对效率进行等级划分。其次,基于生产数据的特征,在员工生产效率预测任务中选择了随机森林集成学习模型,并与其它8个模型进行了综合比较。最后,通过递归式特征消除法,从15个初始特征中筛选出实现模型最大预测性能的最优特征组以优化模型。优化后结果显示,随机森林模型展现出优异的预测性能,在回归任务中,验证集R^(2)值为0.836,而均方根误差值为0.116;在分类任务中,其验证集平衡F分数值为0.823。研究结果表明,使用随机森林模型可以实现产前工人效率的有效预测,预测结果可避免管理者在调度时做出错误决策,同时为生产线的优化和柔性调度提供参考。Objective The significant impact of variations in employee productivity on the balance of apparel production lines has prompted the need for a solution to address the shortfall in achieving targeted productivity levels under manually scheduled operations lacking historical data analysis support.This research aims to utilize machine learning models to predict actual employee efficiency,providing management with valuable insights for goal setting and decision-making to enhance production profitability and prevent erroneous decisions to some extent.Method In order to achieve efficiency prediction,this research conducted on-site surveys at factory A,gathering 526 historical production records from 13 orders.Through feature engineering,15 initial prediction datasets were constructed,and efficiency levels were categorized using quantile division.Subsequently,considering the production data characteristics,RandomForest regression and classification models were selected for efficiency prediction.In order to validate the predictive performance of the model,it was compared with eight other models.Pearson and Spearman correlation coefficient analyses were performed to investigate the impact of variables on the model predictions.Finally,recursive feature elimination was employed to optimize the model by selecting the optimal feature subset from the initial feature set for maximum predictive performance.Results Using a random split function,20%of the prediction dataset was set aside for validation,while the remaining 80%was divided into training and testing sets for ten-fold cross-validation.R^(2)and RMSE were chosen as regression metrics,and F 1 score was selected as the classification metric.The RandomForest regression model demonstrated the optimal predictive performance,showing the smallest range of fit and root mean square error in ten-fold cross-validation,with a fitting goodness value of 0.826 and an RMSE value of 0.126.In the classification task,the random forest model exhibited higher predictive performance compared

关 键 词：服装生产数据 机器学习 产前效率预测 递归式特征消除 柔性调度 

分 类 号：TS941.19[轻工技术与工程—服装设计与工程]