This paper is concerned with the use of fuzzy sets theory, well suited for nuanced human behavior, to extend the applicability of linear programming in a way to let both randomness and fuzziness uncertainties be taken into account. A fuzzy stochastic linear programming problem is considered and ways to handle it suggested. The basic idea behind our approaches is to translate the original problem in deterministic terms via tools provided by fuzzy sets theory to get a classical one. Some conditions for the resulting problems to be solved without extra computational effort are stressed and illustrative examples given.
Let be the set of solutions of a -fuzzy relation equation defined on finite spaces. In this paper we use some algorithms, already introduced in our foregoing work, in order to determine some relations of which have a minimal value of fuzziness measure. By taking into account the fuzzy entropy concept, we build a new lattice structure and some fuzzy topological results are also presented.