Fuzzy convexity and multiobjective convex optimization problems

被引：12
|
作者：
Syau, Yu-Ru
Lee, E. Stanley [1 ]
机构：
[1] Kansas State Univ, Dept Ind & Mfg Syst Engn, Manhattan, KS 66506 USA
[2] Natl Formosa Univ, Dept Informat Management, Yunlin 63201, Taiwan
来源：
COMPUTERS & MATHEMATICS WITH APPLICATIONS | 2006年 / 52卷 / 3-4期
关键词：
fuzzy convexity; fuzzy criterion sets; pareto-optimal decision; multiple objective optimization;
D O I：
10.1016/j.camwa.2006.03.017
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
In this paper, based on the more restrictive definition of fuzzy convexity due to Ammar and Metz [1], several useful composition rules are developed. The advantages in using the more restrictive definition of fuzzy convexity are that local optimality implies global optimality, and that any convex combination of such convex fuzzy sets is also a convex fuzzy set. As shown in this paper, these properties are laking in the usual convex fuzzy sets. In addition, to illustrate the applications in fuzzy convex optimization, two examples in multiple objective programming are considered. (c) 2006 Elsevier Ltd. All rights reserved.
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页码：351 / 362
页数：12
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