基于标准工时预测的衬衣部件模块生产编排优化  

Production scheduling optimization of shirt component module based on standard man-hour prediction

作　　者：盛锡彬 赵崧灵 顾冰菲 SHENG Xibin;ZHAO Songling;GU Bingfei(School of Fashion Design&Engineering,Zhejiang Sci-Tech University,Hangzhou,Zhejiang 310018,China;Digital Intelligence Style and Creative Design Research Center,Zhejiang Sci-Tech University,Hangzhou,Zhejiang 310018,China;Key Laboratory of Silk Culture Heritage and Products Design Digital Technology,Ministry of Culture and Tourism,Hangzhou,Zhejiang 310018,China)

机构地区：[1]浙江理工大学服装学院,浙江杭州310018 [2]浙江理工大学数智风格与创意设计研究中心,浙江杭州310018 [3]丝绸文化传承与产品设计数字化技术文化和旅游部重点实验室,浙江杭州310018

出　　处：《纺织学报》2025年第1期154-162,共9页Journal of Textile Research

基　　金：国家自然科学基金项目(61702461);中国纺织工业联合会应用基础研究项目(J202007);中国纺织工业联合会科技指导性项目(2018079);浙江理工大学科研业务费专项资金资助项目(2020Q051)。

摘　　要：针对模块化智能特性下多品种小批量服装生产系统的快速重构需求,提出一种基于反向传播(BP)神经网络进行模块族工时预测,并用于混合模式部件模块生产编排优化的方法。以企业近几年生产的550款典型衬衫款式为例,基于11类衬衣模块族,通过对标准工时影响因素权重分析,构建BP神经网络预测模型。最终对标准工时模型预测效果进行验证,并基于模块族针对2款衬衣实现混合生产线的工序分配。结果表明:各模块族平均绝对误差均在9 s内,且其中8个模块族的误差值未超过5 s;混款生产模式相较于单款式生产编制的效率均在90%以上,采用模块优化的编制效率可达95.55%,且平滑指数降低了50.09%,说明工时预测及模块化工序分配应用效果较好。本文研究结果可一定程度上满足企业混款组合加工的应用要求,为快速报价、制定生产计划提供参考。Objective For the rapid reconfiguration requirement of multi-variety and small-batch clothing production system under the characteristics of modular intelligence.A method based on back propagation(BP)neural network was proposed to predict module man-hours and optimize the application of mixed mode component module production scheduling.The research results can be utilized to optimize production scheduling,predict man-hours and assign processes,and provide reference for quick quotation and production planning.Method shirt;Taking shirts produced by an enterprise as the research object,a sample set of shirt module man-hour was established,and the influence factors of standard man-hour were analyzed to build a man-hour prediction model.Production of two shirts of the same color and different styles was taken as an example to achieve the arrangement of production by using modules in the mixed assembly line,and the arrangement effect was analyzed.Results In order to measure the accuracy of prediction results more intuitively,the man-hour prediction model was constructed for all module groups and verified one by one.Based on each evaluation index,the prediction accuracy of the model was evaluated,and the prediction results of 11 types of module groups were obtained.From the perspective of model fitting effect,the accuracy of fit of all module groups was above 0.81.From the perspective of the prediction time value of test samples,the average absolute error of each module group was within 9 s and the error value of 8 module groups was not more than 5 s.The model prediction accuracy rate reached more than 90%peaking at 94.89%.Taking the combinatorial splitter module of class 8 module group as an example,the scatter plot was adopted to compare the real value and predicted value data of the test set samples.The values of the two samples were close to each other for most samples,and the error was within the range of±10 s.The paired sample T test was adopted to analyze the error between the actual value and the predicted valu

关 键 词：衬衣 BP神经网络 标准工时 工时预测 模块生产 流水编排 

分 类 号：TS941.17[轻工技术与工程—服装设计与工程]