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Summary In this paper, we define a Kuhn‐Tucker (KT)–pseudoinvex multidimensional control problem. More exactly, we introduce a new condition on the functions, which are involved in a multidimensional control problem, and we prove that a KT‐pseudoinvex multidimensional control problem is characterized such that a KT point is an optimal solution. Thus, we generalize optimality results in known mathematical programming problems. These theoretical results are illustrated with an application.

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In this paper studies the maximal covering location problem, assuming imprecise knowledge of all data involved. The considered problem is modeled from a fuzzy perspective producing suitable fuzzy Pareto solutions. Some properties of the fuzzy model are studied, which validate the equivalent mixed-binary linear multiobjective formulation proposed. A solution algorithm is developed, based on the augmented weighted Tchebycheff method, which produces solutions of guaranteed Pareto optimality. The effectiveness of the algorithm has been tested with a series of computational experiments, whose numerical results are presented and analyzed.

In this work, a new method is presented for locating fuzzy optimal (nondominated) solutions of a fully fuzzy linear programming problem with inequality constraints and triangular fuzzy numbers, not necessarily symmetric, without ranking functions, by means of solving a multiobjective linear problem. An equivalence is proved between the set of nondominated solutions of the fully fuzzy linear programming problem and the set of weakly efficient solutions of the considered and related multiobjective linear problem. An algorithm is provided to generate nondominated solutions of a fully fuzzy linear programming problem, as well as an example to illustrate it.