Distributional Unanimity in Multiobjective Stochastic Linear Programming

Several notions of efficiency are conceivable for the multiobjective stochastic linear programming problem. In this paper, assuming that the problem’s randomness can be described by discrete scenarios with known probabilities and that decision makers’ preferences, although unknown, can be represented by a class of utility functions, we examine a set of strongly efficient solutions, the unanimous solutions. We state inclusion relations between this and other classes of efficient solutions (admissible and advocated solutions) previously studied. Under plausible assumptions about decision makers’ risk attitudes, we examine how candidates for unanimity can be generated and then tested.