Robust optimization under softness in a fuzzy linear programming problem

被引:72
作者
Inuiguchi, M [1 ]
Sakawa, M [1 ]
机构
[1] Hiroshima Univ, Higashihiroshima 739, Japan
关键词
fuzzy linear programming; robustness; soft optimality; necessity; relaxation procedure; bisection method; simplex method;
D O I
10.1016/S0888-613X(97)10002-0
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this paper, we discuss the softness and the robustness of the optimality in the setting of linear programming problems with a fuzzy objective function. A fuzzy goal defined on the deviation from the optimal value is introduced, in order to define the soft-optimal solution. Fuzzy coefficients are regarded as possibility distributions. A necessity measure based on the possibility distribution is used for defining a necessarily optimal solution, i.e., a robust-optimal solution. Since a necessarily optimal solution does not exist in many cases, a necessarily soft-optimal solution is defined. A solution algorithm for the best necessarily soft-optimal solution is proposed. (C) 1998 Elsevier Science Inc.
引用
收藏
页码:21 / 34
页数:14
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