{"title":"基于e模型和可能性测度的多目标模糊随机线性规划","authors":"H. Katagiri, M. Sakawa, H. Ishii","doi":"10.1109/NAFIPS.2001.944430","DOIUrl":null,"url":null,"abstract":"The authors deal with multiobjective linear programming problems with fuzzy random variable coefficients. Since the problem is ill-defined due to both fuzziness and randomness, we propose a decision making model based on E-model, which is a useful model in stochastic programming, and a possibility measure. First, we show that the formulated problem is reduced to a multiobjective linear fractional programming problem. After defining a Pareto optimal solution based on the expected value of possibility measure, we construct a solution algorithm for solving a minimax problem. Further, we consider interactive decision making using reference points and give numerical examples.","PeriodicalId":227374,"journal":{"name":"Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2001-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"Multiobjective fuzzy random linear programming using E-model and possibility measure\",\"authors\":\"H. Katagiri, M. Sakawa, H. Ishii\",\"doi\":\"10.1109/NAFIPS.2001.944430\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The authors deal with multiobjective linear programming problems with fuzzy random variable coefficients. Since the problem is ill-defined due to both fuzziness and randomness, we propose a decision making model based on E-model, which is a useful model in stochastic programming, and a possibility measure. First, we show that the formulated problem is reduced to a multiobjective linear fractional programming problem. After defining a Pareto optimal solution based on the expected value of possibility measure, we construct a solution algorithm for solving a minimax problem. Further, we consider interactive decision making using reference points and give numerical examples.\",\"PeriodicalId\":227374,\"journal\":{\"name\":\"Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569)\",\"volume\":\"87 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2001-07-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NAFIPS.2001.944430\",\"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 Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NAFIPS.2001.944430","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Multiobjective fuzzy random linear programming using E-model and possibility measure
The authors deal with multiobjective linear programming problems with fuzzy random variable coefficients. Since the problem is ill-defined due to both fuzziness and randomness, we propose a decision making model based on E-model, which is a useful model in stochastic programming, and a possibility measure. First, we show that the formulated problem is reduced to a multiobjective linear fractional programming problem. After defining a Pareto optimal solution based on the expected value of possibility measure, we construct a solution algorithm for solving a minimax problem. Further, we consider interactive decision making using reference points and give numerical examples.