{"title":"管理多标准决策的工具和技术","authors":"P. Fleming","doi":"10.1109/MCDM.2007.369411","DOIUrl":null,"url":null,"abstract":"Summary form only given. Design problems arising in business and industry can often be conveniently formulated as multi-criteria decision-making problems. However, these often comprise a relatively large number of criteria. Through our close association with designers in industry and business we have devised a range of machine learning tools and associated techniques to address the special requirements of many-criteria decision-making. These include visualisation and analysis tools to aid the identification of features such as \"hot-spots\" and non-competing criteria, preference articulation techniques to assist in interrogating the search region of interest and methods to address the special computational demands of these problems. With the aid of test problems and real design exercises, we will demonstrate these approaches and also discuss alternative methods","PeriodicalId":306422,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making","volume":"16 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Tools and Techniques for Managing Many-Criteria Decision-Making\",\"authors\":\"P. Fleming\",\"doi\":\"10.1109/MCDM.2007.369411\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Summary form only given. Design problems arising in business and industry can often be conveniently formulated as multi-criteria decision-making problems. However, these often comprise a relatively large number of criteria. Through our close association with designers in industry and business we have devised a range of machine learning tools and associated techniques to address the special requirements of many-criteria decision-making. These include visualisation and analysis tools to aid the identification of features such as \\\"hot-spots\\\" and non-competing criteria, preference articulation techniques to assist in interrogating the search region of interest and methods to address the special computational demands of these problems. With the aid of test problems and real design exercises, we will demonstrate these approaches and also discuss alternative methods\",\"PeriodicalId\":306422,\"journal\":{\"name\":\"2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making\",\"volume\":\"16 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MCDM.2007.369411\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MCDM.2007.369411","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Tools and Techniques for Managing Many-Criteria Decision-Making
Summary form only given. Design problems arising in business and industry can often be conveniently formulated as multi-criteria decision-making problems. However, these often comprise a relatively large number of criteria. Through our close association with designers in industry and business we have devised a range of machine learning tools and associated techniques to address the special requirements of many-criteria decision-making. These include visualisation and analysis tools to aid the identification of features such as "hot-spots" and non-competing criteria, preference articulation techniques to assist in interrogating the search region of interest and methods to address the special computational demands of these problems. With the aid of test problems and real design exercises, we will demonstrate these approaches and also discuss alternative methods