Three methods are discussed here for selecting an optimal decision vector in a linear programming framework, where some stochastic components are present. The methods deal with problems of efficient estimation, problems of selecting the bent population and the game-theoretic solutions. The complexity of the problem of characterizing the so-called best optimal solution is focused here.