  The experimental evaluation of algorithms results in a large set of data
which generally do not follow a normal distribution or are not heteroscedastic.
Besides, some of its entries may be missing, due to the inability of an
algorithm to find a feasible solution until a time limit is met. Those
characteristics restrict the statistical evaluation of computational
experiments. This work proposes a bi-objective lexicographical ranking scheme
to evaluate datasets with such characteristics. The output ranking can be used
as input to any desired statistical test. We used the proposed ranking scheme
to assess the results obtained by the Iterative Rounding heuristic (IR). A
Friedman's test and a subsequent post-hoc test carried out on the ranked data
demonstrated that IR performed significantly better than the Feasibility Pump
heuristic when solving 152 benchmark problems of Nonconvex Mixed-Integer
Nonlinear Problems. However, is also showed that the RECIPE heuristic was
significantly better than IR when solving the same benchmark problems.
