Pareto-optimal solutions for multi-objective flexible linear programming

被引：2
|
作者：
Dubey, Dipti [1 ]
Mehra, Aparna [2 ]
机构：
[1] Indian Stat Inst, Delhi Ctr, Stat Qual Control & Operat Res Unit, 7 SJS Sansanwal Marg, New Delhi 110016, India
[2] Indian Inst Technol Delhi, Dept Math, Hauz Khas, New Delhi, India
来源：
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS | 2016年 / 30卷 / 01期
关键词：
Fuzzy mathematical programming; flexible constraints; flexible constraint with interval uncertainty; aggregation operator; Pareto-optimal solution; FUZZY SET-THEORY; OPTIMIZATION; UNCERTAINTY; EXTENSIONS; SYSTEMS;
D O I：
10.3233/IFS-151778
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The aim of this paper is twofold. Firstly, to define a solution concept of Pareto-optimality for a multi-objective flexible linear programming (MOFLP) problem (or multi-objective fuzzy linear programming problem) and design a method to extract a Pareto-optimal solution of MOFLP problem from the set of optimal solutions of equivalent optimization problem formulated by Dubey and Mehra (2013). Secondly, to extend this study to multi-objective linear programming problem involving hard and flexible constraints with interval uncertainty. A flexible constraint with interval uncertainty generalizes a flexible constraint by allowing preferences to be expressed in the form of intervals. An optimistic-pessimistic approach is proposed to solve multi-objective flexible linear programming with interval uncertainty (MOFLPIU) using an interval-valued fuzzy set representation and the Hurwicz optimism-pessimism criterion.
引用
收藏
页码：535 / 546
页数：12
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