Open AccessOn robust weakly $ \varepsilon $-efficient solutions for multi-objective fractional programming problems under data uncertainty
Author(s) -
Shima Soleimani Manesh,
Mansour Saraj,
Mahmood Alizadeh,
Maryam Momeni
Publication year - 2021
Publication title -
aims mathematics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.329
H-Index - 15
ISSN - 2473-6988
DOI - 10.3934/math.2022132
Subject(s) - fractional programming , saddle point , mathematics , function (biology) , parametric statistics , mathematical optimization , robust optimization , convex function , regular polygon , saddle , nonlinear programming , physics , statistics , geometry , nonlinear system , quantum mechanics , evolutionary biology , biology
In this study, we use the robust optimization techniques to consider a class of multi-objective fractional programming problems in the presence of uncertain data in both of the objective function and the constraint functions. The components of the objective function vector are reported as ratios involving a convex non-negative function and a concave positive function. In addition, on applying a parametric approach, we establish $ \varepsilon $-optimality conditions for robust weakly $ \varepsilon $-efficient solution. Furthermore, we present some theorems to obtain a robust $ \varepsilon $-saddle point for uncertain multi-objective fractional problem.