Second-order contingent derivative of the perturbation map in multiobjective optimization.
Second-order optimality conditions for nondominated solutions of multiobjective programming with data
We examine new second-order necessary conditions and sufficient conditions which characterize nondominated solutions of a generalized constrained multiobjective programming problem. The vector-valued criterion function as well as constraint functions are supposed to be from the class . Second-order optimality conditions for local Pareto solutions are derived as a special case.
Sensitivity analysis for parametric vector optimization problems using differential equations approach.
Separation of convex polyhedral sets with column parameters
Separation is a famous principle and separation properties are important for optimization theory and various applications. In practice, input data are rarely known exactly and it is advisable to deal with parameters. In this article, we are concerned with the basic characteristics (existence, description, stability etc.) of separating hyperplanes of two convex polyhedral sets depending on parameters. We study the case, when parameters are situated in one column of the constraint matrix from the...
Separation theorems for sets in product spaces and equivalent assertions
Sobre soluciones óptimas en problemas de optimización multiobjetivo.
Estudiamos los principales tipos de conceptos de óptimo considerados en problemas de optimización multiobjetivo, cuando la ordenación de alternativas se regula mediante un cono K convexo: soluciones K-maximales, débilmente K-maximales, fuertemente K-maximales, propiamente K-maximales. Damos caracterizaciones en problemas generales de optimización vectorial y condiciones suficientes en problemas de maximización de funciones de valor vectoriales y escalares, particularizando después al caso de conos...
Soluciones propias en la teoría de la dominación.
Se relacionan varios conceptos de "punto propiamente no dominado", introducidos para eliminar soluciones no dominadas "poco deseables", dándose condiciones para las distintas implicaciones y equivalencias.
Solution approaches to large shift scheduling problems
This paper considers large shift scheduling problems with different shift start times and lengths, fractionable breaks and work stretch duration restrictions. Two solution approaches are proposed to solve the problems over a multiple-day planning horizon. The first approach is based on a local branching strategy and the second one is based on a temporal decomposition of the problem. Local branching is very efficient in finding good feasible solutions when compared to a classical branch-and-bound...
Solving a possibilistic linear program through compromise programming.
In this paper we propose a method to solve a linear programming problem involving fuzzy parameters whose possibility distributions are given by fuzzy numbers. To address the above problem we have used a preference relationship of fuzzy numbers that leads us to a solving method that produces the so-called α-degree feasible solutions. It must be pointed out that the final solution of the problem depends critically on this degree of feasibility, which is in conflict with the optimal value of the objective...
Solving single and multiobjective models for an assembly line design problem through ant colony algorithms.
Some characterizations of ideal points in vector optimization problems.
Some relations between variational-like inequalities and efficient solutions of certain nonsmooth optimization problems.
Stability and accuracy functions in multicriteria combinatorial optimization
Stochastic goal programming wth recourse
In this article we discuss several alternative formulations for Stochastic Goal Programming. Only one of these models, which is a particular case of the Stochastic Programs with Recourse, is also compatible with Bayesian Decision Theory. Moreover, it is posible to approximate its solutions by means of an iterative algorithm.
Strong convergence of a new iterative method for infinite family of generalized equilibrium and fixed-point problems of nonexpansive mappings in Hilbert spaces.
Strongly proper optimums and maximal optimization in multiobjective programming.
Structure of efficient sets for strictly quasi convex objectives.
Sufficient conditions and duality for multiobjective variational problems with generalized B-invexity
Sufficient conditions of optimality for multiobjective optimization problems with γ-paraconvex data
We study multiobjective optimization problems with γ-paraconvex multifunction data. Sufficient optimality conditions for unconstrained and constrained problems are given in terms of contingent derivatives.
Sufficient Second Order Optimality Conditions for C^1 Multiobjective Optimization Problems
2000 Mathematics Subject Classification: Primary 90C29; Secondary 90C30.In this work, we use the notion of Approximate Hessian introduced by Jeyakumar and Luc [19], and a special scalarization to establish sufficient optimality conditions for constrained multiobjective optimization problems. Throughout this paper, the data are assumed to be of class C^1, but not necessarily of class C^(1.1).