{"title":"模糊代价系数线性规划问题的几个新结果","authors":"A. Ebrahimnejad","doi":"10.2004/WJST.V10I2.424","DOIUrl":null,"url":null,"abstract":"The fuzzy primal simplex method proposed by Mahdavi-Amiri et al. and the fuzzy dual simplex method proposed by SH Nasseri and A Ebrahimnejad are two current procedures for solving linear programming problems with fuzzy cost coefficients known as reduced fuzzy numbers linear programming (RFNLP) problems. In this paper, we prove that in the absence of degeneracy these fuzzy methods stop in a finite numbers of iterations. We also prove the fundamental theorem of linear programming in a crisp environment to a fuzzy one. Finally, we illustrate our proof by use of a numerical example.","PeriodicalId":38275,"journal":{"name":"Walailak Journal of Science and Technology","volume":"10 1","pages":"191-199"},"PeriodicalIF":0.0000,"publicationDate":"2013-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Some New Results in Linear Programming Problems with Fuzzy Cost Coefficients\",\"authors\":\"A. Ebrahimnejad\",\"doi\":\"10.2004/WJST.V10I2.424\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The fuzzy primal simplex method proposed by Mahdavi-Amiri et al. and the fuzzy dual simplex method proposed by SH Nasseri and A Ebrahimnejad are two current procedures for solving linear programming problems with fuzzy cost coefficients known as reduced fuzzy numbers linear programming (RFNLP) problems. In this paper, we prove that in the absence of degeneracy these fuzzy methods stop in a finite numbers of iterations. We also prove the fundamental theorem of linear programming in a crisp environment to a fuzzy one. Finally, we illustrate our proof by use of a numerical example.\",\"PeriodicalId\":38275,\"journal\":{\"name\":\"Walailak Journal of Science and Technology\",\"volume\":\"10 1\",\"pages\":\"191-199\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-03-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Walailak Journal of Science and Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2004/WJST.V10I2.424\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Multidisciplinary\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Walailak Journal of Science and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2004/WJST.V10I2.424","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Multidisciplinary","Score":null,"Total":0}
Some New Results in Linear Programming Problems with Fuzzy Cost Coefficients
The fuzzy primal simplex method proposed by Mahdavi-Amiri et al. and the fuzzy dual simplex method proposed by SH Nasseri and A Ebrahimnejad are two current procedures for solving linear programming problems with fuzzy cost coefficients known as reduced fuzzy numbers linear programming (RFNLP) problems. In this paper, we prove that in the absence of degeneracy these fuzzy methods stop in a finite numbers of iterations. We also prove the fundamental theorem of linear programming in a crisp environment to a fuzzy one. Finally, we illustrate our proof by use of a numerical example.
期刊介绍:
The Walailak Journal of Science and Technology (Walailak J. Sci. & Tech. or WJST), is a peer-reviewed journal covering all areas of science and technology, launched in 2004. It is published 12 Issues (Monthly) by the Institute of Research and Innovation of Walailak University. The scope of the journal includes the following areas of research : - Natural Sciences: Biochemistry, Chemical Engineering, Chemistry, Materials Science, Mathematics, Molecular Biology, Physics and Astronomy. -Life Sciences: Allied Health Sciences, Biomedical Sciences, Dentistry, Genetics, Immunology and Microbiology, Medicine, Neuroscience, Nursing, Pharmaceutics, Psychology, Public Health, Tropical Medicine, Veterinary. -Applied Sciences: Agricultural, Aquaculture, Biotechnology, Computer Science, Cybernetics, Earth and Planetary, Energy, Engineering, Environmental, Food Science, Information Technology, Meat Science, Nanotechnology, Plant Sciences, Systemics