现代纺织技术2025，Vol.33Issue(3)：81-90,10.DOI:10.19398/j.att.202405006
基于加工时间异质性的裤装吊挂流水线平衡优化
Balance optimization of pants'hanging assembly lines based on the heterogeneity of processing time
摘要
Abstract
Most research on apparel workshop scheduling assumes that each workstation has the same production load.In reality,during the initial scheduling phase prior to production in apparel manufacturing enterprises,managers indeed consider the production efficiency of each worker to be the same.Although this relaxation model facilitates the ease of constructing of objectives,handling constraints,and improving algorithmic efficiency across various production scenarios,it deviates from reality,resulting in low and unstable actual production line balance rates despite low theoretical production line loss rates.In response to the heterogeneity of workers'processing time,this paper proposes an integrated method based on existing theoretical research,adopting a load coefficient prediction model and the grey wolf optimization(GWO)algorithm to address the imbalance in trouser-hanging production lines caused by worker differences.The objective is to to minimize the smoothness index(SI)to optimize the overall balance of the hanging line. The optimization process comprises two main modules:a neural network-based load coefficient prediction module and a process balancing module based on the GWO algorithm.Prior to prediction,the factors affecting worker efficiency in existing research were expanded to include apparel-specific factors,constructing a framework for workers'processing time heterogeneity factors.In the prediction module,leveraging RFID and IoT technologies,a dataset was constructed from the perspective of personalized influencing factors,focusing on apparel-related factors and some collectible physiological factors.The neural network was trained using Bayesian optimization to achieve optimal parameter settings.The evaluation index MAE of the optimized model in predicting the workstation load coefficient reached 0.091,indicating an acceptable prediction accuracy.With the predicted workstation load coefficients as constraints on workers'processing time,the GWO algorithm was adopted to optimize the problem model.The optimization results indicated that the GWO algorithm demonstrated superior algorithmic performance and stability.This data-driven,concise,and comprehensive intelligent decision-making model can effectively address the issue of production line balancing caused by varying processing time in garment manufacturing.Post-optimization validation results revealed that the balancing index of workstations decreased from 66.4 to 13.8.Therefore,this model significantly enhances the actual balance rate of the production line. This study conducts static scheduling with the load coefficient of the entire order as the prediction target.Dynamic scheduling and static scheduling are not contradictory,and the results can provide a reasonable initial schedule for dynamic scheduling.Additionally,the real-time collection and storage of worker efficiency through RFID and IoT technologies lay the foundation for adopting this model for dynamic scheduling.The neural network prediction function module and optimization module are independent,thus possessing strong generality and integrability,allowing scholars to construct prediction datasets and choose more suitable process balancing optimization algorithms based on the problem's characteristics.关键词
扰动因素/加工时间异质性/灰狼算法/贝叶斯优化/实际平衡率Key words
perturbation factors/processing time heterogeneity/GWO algorithm/Bayesian optimization/actual balance rate分类
轻工业引用本文复制引用
鞠宇,王朝晖,梁志,李博一,倪嘉明..基于加工时间异质性的裤装吊挂流水线平衡优化[J].现代纺织技术,2025,33(3):81-90,10.基金项目
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) （21130750100）
