Goal Programming with fractional objectives can be reduced to mathematical programming with a linear objective under linear and quadratic constraints, thus optimal solutions can be obtained by using existing Global Optimization techniques. However, only heuristic procedures are suggested in the literature on the field.

In this note we explore the practical applicability of a recent algorithm for nonconvex quadratic programming with quadratic constraints for this problem.

Encouraging computational experiences for randomly generated instances with up to 14 fractional objectives are presented.