Stackelberg solutions for random fuzzy two-level linear programming through possibility-based probability model

This paper considers computational methods for obtaining Stackelberg solutions to random fuzzy two-level linear programming problems. Assuming that the decision makers concerns about the probabilities that their own objective function values are smaller than or equal to certain target values, fuzzy goals of the decision makers for the probabilities are introduced. Using the possibility-based probability model to maximize the degrees of possibility with respect to the attained probability, the original random fuzzy two-level programming problems are reduced to deterministic ones. Extended concepts of Stackelberg solutions are introduced and computational methods are also presented. A numerical example is provided to illustrate the proposed method.

► Noncooperative random fuzzy two-level linear programming problems are considered.
► Fuzzy goals are introduced into the formulated two-level programming problems.
► New models are proposed through possibilistic and stochastic programming.
► Extended concepts of Stackelberg solutions under fuzziness and randomness are defined.
► Computational methods for the defined Stackelberg solutions are presented.