Abstract
In this paper, we deal with the resolution of a fuzzy multiobjective programming problem using the level sets optimization. We compare it to other optimization strategies studied until now and we propose an algorithm to identify possible Pareto efficient optimal solutions.
Highlights
The construction of scientific models to describe real situations represents a significant advance in the aid for decision-making, especially in complex problems
Multiobjective Mathematical Programming is really useful in order to model real situations where more than one objective exists
In [16], the same Pareto efficiency notion we propose in this paper is used, and a sufficient optimality conditions for it is proved, but using more restrictive hypotheses on the functions than the ones we suppose here
Summary
The construction of scientific models to describe real situations represents a significant advance in the aid for decision-making, especially in complex problems. In [13], the authors explain how interesting it is to use the Nearest Interval Approximation Operator to solve a multiobjective programming problem with fuzzy objective functions, to reflect reality and have excellent computational behavior. They established a sufficient Karush–Kuhn–Tucker type of Pareto optimality conditions, using continuously gH-differentiable functions where.
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