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.
Citation: Shima Soleimani Manesh, Mansour Saraj, Mahmood Alizadeh, Maryam Momeni. On robust weakly $ \varepsilon $-efficient solutions for multi-objective fractional programming problems under data uncertainty[J]. AIMS Mathematics, 2022, 7(2): 2331-2347. doi: 10.3934/math.2022132
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.
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Shima Soleimani Manesh, Mansour Saraj, Mahmood Alizadeh, Maryam Momeni. On robust weakly $ \varepsilon $-efficient solutions for multi-objective fractional programming problems under data uncertainty[J]. AIMS Mathematics, 2022, 7(2): 2331-2347. doi: 10.3934/math.2022132
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