{"title":"基于GA-PLS的产品顾客满意度优化模型","authors":"H. Ertian, Ni Yangdan","doi":"10.1109/IFITA.2010.341","DOIUrl":null,"url":null,"abstract":"In order to solve the difficulty — how to choose elements of product performance and price — and the distortion problem caused by the multi-variable correlation, this paper proposes a optimized model of product customer satisfaction (PCS) based on genetic algorithms and partial least squares. The model not only solves the distortion problem due to multicollinearity, but also provides multiple models for policy makers. At last, the model is implemented in optimizing mobile phone’s satisfaction. From the result we can determine which element has the greatest impact on the overall satisfaction, then improve the overall product satisfaction by improving the element. The result indicates the method is effective, which has offered the important basis for optimization product design parameters.","PeriodicalId":393802,"journal":{"name":"2010 International Forum on Information Technology and Applications","volume":"398 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Product Customer Satisfaction Optimized Model Based on GA-PLS\",\"authors\":\"H. Ertian, Ni Yangdan\",\"doi\":\"10.1109/IFITA.2010.341\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In order to solve the difficulty — how to choose elements of product performance and price — and the distortion problem caused by the multi-variable correlation, this paper proposes a optimized model of product customer satisfaction (PCS) based on genetic algorithms and partial least squares. The model not only solves the distortion problem due to multicollinearity, but also provides multiple models for policy makers. At last, the model is implemented in optimizing mobile phone’s satisfaction. From the result we can determine which element has the greatest impact on the overall satisfaction, then improve the overall product satisfaction by improving the element. The result indicates the method is effective, which has offered the important basis for optimization product design parameters.\",\"PeriodicalId\":393802,\"journal\":{\"name\":\"2010 International Forum on Information Technology and Applications\",\"volume\":\"398 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-07-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 International Forum on Information Technology and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IFITA.2010.341\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 International Forum on Information Technology and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IFITA.2010.341","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Product Customer Satisfaction Optimized Model Based on GA-PLS
In order to solve the difficulty — how to choose elements of product performance and price — and the distortion problem caused by the multi-variable correlation, this paper proposes a optimized model of product customer satisfaction (PCS) based on genetic algorithms and partial least squares. The model not only solves the distortion problem due to multicollinearity, but also provides multiple models for policy makers. At last, the model is implemented in optimizing mobile phone’s satisfaction. From the result we can determine which element has the greatest impact on the overall satisfaction, then improve the overall product satisfaction by improving the element. The result indicates the method is effective, which has offered the important basis for optimization product design parameters.