Generally, an engineering design problem has multiple objective functions. Some of these problems can be formulated as Multiobjective geometric programming models. On the other hand, often in the real world, coefficients of the objective functions are not known precisely. Coefficients may be interpreted as fuzzy numbers, which lead to a Multiobjective geometric programming with fuzzy parameters. In this paper, we solve the Multiobjective geometric programming problem with fuzzy parameters by applying a linear ranking function. The linear ranking function is used to compare fuzzy numbers. Finally, a numerical example is given.

In this paper, the concept of ¶*-quasiconvexity is introduced by using convexifactors. Mond-Weir-type and Schaible-type duals are associated with a Multiobjective fractional programming problem, and various duality results are established under the assumptions of ¶*-pseudoconvexity and ¶*-quasiconvexity.

In this paper, a new class of nonconvex differentiable Multiobjective programming problems involving n-set functions with both inequality and equality constraints is considered. Then, under V-r-convexity and/or generalized V-r-convexity hypotheses, several suffcient optimality conditions, saddle point criteria and various mixed duality theorems are proved for such not necessarily convex vector optimization problems involving n-set functions.