{"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}
引用次数: 12
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.