Interval linear optimization problems with fuzzy inequality constraints

In many real-life situations, we come across problems with imprecise input values. Imprecisions are dealt with by various ways. One of them is interval based approach in which we model imprecise quantities by intervals, and suppose that the quantities may vary independently and simultaneously within their intervals. In most optimization problems, they are formulated using imprecise parameters. Such parameters can be considered as fuzzy intervals, and the optimization tasks with interval cost function are obtained. When realistic problems are formulated, a set of intervals may appear as coefficients in the objective function or the constraints of a linear programming problem. In this paper, we introduce a new method for solving linear optimization problems with interval parameters in the objective function and the inequality constraints, and we show the efficiency of the proposed method by presenting a numerical example.