[1]林佳红,方丽英*.基于决策树算法的裤子生产模块工时回归预测[J].服装学报,2025,10(02):116-122.
　LIN Jiahong,FANG Liying*.Regression Prediction of Man-Hour for the Trousers Production Module Based on Decision Tree Algorithm[J].Journal of Clothing Research,2025,10(02):116-122.
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基于决策树算法的裤子生产模块工时回归预测
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《服装学报》[ISSN:2096-1928/CN:32-1864/TS]

卷:
第10卷
期数:
2025年02期
页码:
116-122
栏目:
服装智造
出版日期:
2025-04-30

文章信息/Info

Title:
Regression Prediction of Man-Hour for the Trousers Production Module Based on Decision Tree Algorithm
作者:
林佳红1;  方丽英*2
1.浙江理工大学 服装学院,浙江 杭州 310018; 2.浙江理工大学 国际教育学院,浙江 杭州 310018
Author(s):
LIN Jiahong1;  FANG Liying*2
(1.School of Fashion Design and Engineering,Zhejiang Sci-Tech University,Hangzhou 310018,China; 2.School of International Education,Zhejiang Sci-Tech University,Hangzhou 310018,China
分类号:
TS 941.63; TS 941.714.2
文献标志码:
A
摘要:
服装标准工时是企业生产编排、预估生产周期、考核员工绩效的依据。当前服装款式复杂多变,使用传统定额方法需耗费大量的时间成本,阻碍了企业的生产排程与调度。以裤子品类为例,提出基于决策树回归的模块工时预测方法,根据企业所生产的款式特点进行模块划分,生成模块编码与模块工时,并建立裤子模块工时数据库。同时,在机器学习库的决策树回归模型中输入服装特征编码,可预测出各模块工时,为企业模块化单元的集成应用构建工时基础。结果表明,模型各模块工时的平均预测准确率达90.00%,总工时的预测准确率达94.96%,可为服装企业工时定额提供一种高效可行的方案。
Abstract:
Garment standard man-hour serves as the foundation for enterprise production scheduling, estimating production cycles, and evaluating employee performance. The increasing complexity and diversity of garment styles necessitate the adoption of efficient methods to avoid the high time costs associated with traditional quota-based approaches, thereby improving production planning and scheduling in enterprises. By focusing on trousers as a case study, this research introduces a module man-hour forecasting methodology leveraging decision tree regression. Based on the style characteristics of garments manufactured by enterprises, modules are defined, producing module codes and corresponding man-hours, and assembling a database for trouser module man-hours. Furthermore, inputting garment feature codes into the decision tree regression model within a machine learning library enables the prediction of individual module man-hours, providing a basis for integrating modular units within enterprises. The results demonstrate that the model achieves an average prediction accuracy of 90.00% for individual module man-hours and a total man-hour prediction accuracy of 94.96%, offering garment enterprises an efficient and practical solution for man-hour quota determination.

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更新日期/Last Update: 2025-04-30