{"title":"绩效评价的模糊逻辑方法","authors":"S. Ammar, R. Wright","doi":"10.1109/ISUMA.1995.527701","DOIUrl":null,"url":null,"abstract":"Client satisfaction surveys are a growing part of performance evaluation for both individuals and corporate units. At one Northeast public utility, when these surveys became a major part of performance evaluation, concerns were raised about the validity of current procedures for analyzing this data. A research and development effort was begun to see how well fuzzy logic could be used to analyze this inherently imprecise data. In this paper, we describe the current procedures and their obvious defects, a preliminary model which corrects the worst deficiencies, and finally the R&D supported model which was developed using a fuzzy rule-based system.","PeriodicalId":298915,"journal":{"name":"Proceedings of 3rd International Symposium on Uncertainty Modeling and Analysis and Annual Conference of the North American Fuzzy Information Processing Society","volume":"47 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1995-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"A fuzzy logic approach to performance evaluation\",\"authors\":\"S. Ammar, R. Wright\",\"doi\":\"10.1109/ISUMA.1995.527701\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Client satisfaction surveys are a growing part of performance evaluation for both individuals and corporate units. At one Northeast public utility, when these surveys became a major part of performance evaluation, concerns were raised about the validity of current procedures for analyzing this data. A research and development effort was begun to see how well fuzzy logic could be used to analyze this inherently imprecise data. In this paper, we describe the current procedures and their obvious defects, a preliminary model which corrects the worst deficiencies, and finally the R&D supported model which was developed using a fuzzy rule-based system.\",\"PeriodicalId\":298915,\"journal\":{\"name\":\"Proceedings of 3rd International Symposium on Uncertainty Modeling and Analysis and Annual Conference of the North American Fuzzy Information Processing Society\",\"volume\":\"47 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1995-03-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of 3rd International Symposium on Uncertainty Modeling and Analysis and Annual Conference of the North American Fuzzy Information Processing Society\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISUMA.1995.527701\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of 3rd International Symposium on Uncertainty Modeling and Analysis and Annual Conference of the North American Fuzzy Information Processing Society","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISUMA.1995.527701","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Client satisfaction surveys are a growing part of performance evaluation for both individuals and corporate units. At one Northeast public utility, when these surveys became a major part of performance evaluation, concerns were raised about the validity of current procedures for analyzing this data. A research and development effort was begun to see how well fuzzy logic could be used to analyze this inherently imprecise data. In this paper, we describe the current procedures and their obvious defects, a preliminary model which corrects the worst deficiencies, and finally the R&D supported model which was developed using a fuzzy rule-based system.