Yibing Shao, Xiaofeng Ji, Menglin Zheng, Caiya Chen
{"title":"基于粒子群优化的支持向量机缝纫过程标准时间预测","authors":"Yibing Shao, Xiaofeng Ji, Menglin Zheng, Caiya Chen","doi":"10.2478/aut-2021-0037","DOIUrl":null,"url":null,"abstract":"Abstract Standard time is a key indicator to measure the production efficiency of the sewing department, and it plays a vital role in the production forecast for the apparel industry. In this article, the grey correlation analysis was adopted to identify seven sources as the main influencing factors for determination of the standard time in the sewing process, which are sewing length, stitch density, bending stiffness, fabric weight, production quantity, drape coefficient, and length of service. A novel forecasting model based on support-vector machine (SVM) with particle swarm optimization (PSO) is then proposed to predict the standard time of the sewing process. On the ground of real data from a clothing company, the proposed forecasting model is verified by evaluating the performance with the squared correlation coefficient (R2) and mean square error (MSE). Using the PSO-SVM method, the R2 and MSE are found to be 0.917 and 0.0211, respectively. In conclusion, the high accuracy of the PSO-SVM method presented in this experiment states that the proposed model is a reliable forecasting tool for determination of standard time and can achieve good predicted results in the sewing process.","PeriodicalId":49104,"journal":{"name":"Autex Research Journal","volume":"22 1","pages":"290 - 297"},"PeriodicalIF":1.1000,"publicationDate":"2021-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Prediction of Standard Time of the Sewing Process using a Support Vector Machine with Particle Swarm Optimization\",\"authors\":\"Yibing Shao, Xiaofeng Ji, Menglin Zheng, Caiya Chen\",\"doi\":\"10.2478/aut-2021-0037\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract Standard time is a key indicator to measure the production efficiency of the sewing department, and it plays a vital role in the production forecast for the apparel industry. In this article, the grey correlation analysis was adopted to identify seven sources as the main influencing factors for determination of the standard time in the sewing process, which are sewing length, stitch density, bending stiffness, fabric weight, production quantity, drape coefficient, and length of service. A novel forecasting model based on support-vector machine (SVM) with particle swarm optimization (PSO) is then proposed to predict the standard time of the sewing process. On the ground of real data from a clothing company, the proposed forecasting model is verified by evaluating the performance with the squared correlation coefficient (R2) and mean square error (MSE). Using the PSO-SVM method, the R2 and MSE are found to be 0.917 and 0.0211, respectively. In conclusion, the high accuracy of the PSO-SVM method presented in this experiment states that the proposed model is a reliable forecasting tool for determination of standard time and can achieve good predicted results in the sewing process.\",\"PeriodicalId\":49104,\"journal\":{\"name\":\"Autex Research Journal\",\"volume\":\"22 1\",\"pages\":\"290 - 297\"},\"PeriodicalIF\":1.1000,\"publicationDate\":\"2021-07-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Autex Research Journal\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://doi.org/10.2478/aut-2021-0037\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"MATERIALS SCIENCE, TEXTILES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Autex Research Journal","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.2478/aut-2021-0037","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MATERIALS SCIENCE, TEXTILES","Score":null,"Total":0}
Prediction of Standard Time of the Sewing Process using a Support Vector Machine with Particle Swarm Optimization
Abstract Standard time is a key indicator to measure the production efficiency of the sewing department, and it plays a vital role in the production forecast for the apparel industry. In this article, the grey correlation analysis was adopted to identify seven sources as the main influencing factors for determination of the standard time in the sewing process, which are sewing length, stitch density, bending stiffness, fabric weight, production quantity, drape coefficient, and length of service. A novel forecasting model based on support-vector machine (SVM) with particle swarm optimization (PSO) is then proposed to predict the standard time of the sewing process. On the ground of real data from a clothing company, the proposed forecasting model is verified by evaluating the performance with the squared correlation coefficient (R2) and mean square error (MSE). Using the PSO-SVM method, the R2 and MSE are found to be 0.917 and 0.0211, respectively. In conclusion, the high accuracy of the PSO-SVM method presented in this experiment states that the proposed model is a reliable forecasting tool for determination of standard time and can achieve good predicted results in the sewing process.
期刊介绍:
Only few journals deal with textile research at an international and global level complying with the highest standards.
Autex Research Journal has the aim to play a leading role in distributing scientific and technological research results on textiles publishing original and innovative papers after peer reviewing, guaranteeing quality and excellence.
Everybody dedicated to textiles and textile related materials is invited to submit papers and to contribute to a positive and appealing image of this Journal.