Decisions with uncertain alternatives

This paper analyses decision models with an uncertain set of alternatives defined by a deterministic objective function and constraints with uncertain coefficients. Here, in contrast to decisions under risk, the stochastic distribution of uncertain coefficients is not known. On the basis of an uncertain multiobjective decision model, we define efficient alternatives and formulate deterministic surrogate models. Uncertain decision models with recourse are introduced, and we present solution concepts that combine approaches from both multiobjective decision making and decision models with uncertain objective functions. These approaches are discussed in relation to stochastic and fuzzy programming and to models with linear partial information. In addition, ramifications of this particular approach are explored.