Approximations and Sensitivity in Multiobjective Resource Allocation

This paper addresses the modeling of resource allocation planning problems having uncertainties, multiple competing objectives, organizational constraints, and continuous decision variables. The application of multiobjective decision analysis leads to a nonlinear programming formulation in which the objective function is the expectation of a multiattribute utility function. If a set of independence conditions holds, this function can be decomposed into appropriately scaled sums and products of one-dimensional expected utility functions. Approximations that greatly simplify the data acquisition for, and the construction of, the one-dimensional expected utility functions are discussed. Sensitivity analyses indicate that optimal solutions to such models are robust with respect to changes in the required data, but may be seriously in error if certain popular, but overly simplified, forms for the objective function are assumed.