Abstract
In this paper, possibilistic linear programming problems with possibility distribution coefficient are discussed. In these problems, uncertainety is included in each coefficient, so evaluation methods for the optimality are required. The evaluation methods have been proposed based on possibilty and necessity measures in conventional researches. But, the coefficients of an objective function have been treated independently as fuzzy numbers or intervals, therefore, the relation among each coefficient has not been considered. In this paper, the relation among coefficients with uncertainty is represented by a normal possiblity distribution. And two algorithms for the evaluations are proposed, which are shown to be deduced to quadratic and linear programming problems. Finally numerical examples are shown to demonstrate our algorithms.
