{"title":"多目标线性规划中代表性非支配点的寻找","authors":"L. Shao, M. Ehrgott","doi":"10.1109/MCDM.2007.369446","DOIUrl":null,"url":null,"abstract":"In this paper we address the problem of finding well distributed nondominated points for an MOLP. We propose a method which combines the global shooting and normal boundary intersection methods. It overcomes the limitation of normal boundary intersection method that parts of the non-dominated set may be missed. We prove that this method produces evenly distributed nondominated points. Moreover, the coverage error and the uniformity level can be measured. Finally, we apply this method to an optimization problem in radiation therapy and show results for some clinical cases","PeriodicalId":306422,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"18","resultStr":"{\"title\":\"Finding Representative Nondominated Points in Multiobjective Linear Programming\",\"authors\":\"L. Shao, M. Ehrgott\",\"doi\":\"10.1109/MCDM.2007.369446\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we address the problem of finding well distributed nondominated points for an MOLP. We propose a method which combines the global shooting and normal boundary intersection methods. It overcomes the limitation of normal boundary intersection method that parts of the non-dominated set may be missed. We prove that this method produces evenly distributed nondominated points. Moreover, the coverage error and the uniformity level can be measured. Finally, we apply this method to an optimization problem in radiation therapy and show results for some clinical cases\",\"PeriodicalId\":306422,\"journal\":{\"name\":\"2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making\",\"volume\":\"23 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"18\",\"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.2007.369446\",\"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.2007.369446","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Finding Representative Nondominated Points in Multiobjective Linear Programming
In this paper we address the problem of finding well distributed nondominated points for an MOLP. We propose a method which combines the global shooting and normal boundary intersection methods. It overcomes the limitation of normal boundary intersection method that parts of the non-dominated set may be missed. We prove that this method produces evenly distributed nondominated points. Moreover, the coverage error and the uniformity level can be measured. Finally, we apply this method to an optimization problem in radiation therapy and show results for some clinical cases