Stochastic MOLP with incomplete information: An interactive approach with recourse

Numerous multiobjective linear programming (MOLP) methods have been proposed in the last two decades, but almost all for contexts where the parameters of problems are deterministic. However, in many real situations, parameters of a stochastic nature arise. In this paper, the authors suppose that the decision-maker is confronted with a situation of partial uncertainty and is in possession of incomplete information about the stochastic parameters of the problem, this information allowing only the limits of variation of these parameters and eventually their central values to be specified. For such situations, the authors propose a multiobjective stochastic linear programming methodology; it implies the transformation of the stochastic objective functions and constraints in order to obtain an equivalent deterministic MOLP problem and the solving of this last problem by an interactive approach derived from the STEM method. The present methodology is illustrated by a didactical example.