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

页码：535 / 546

页数：12

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