Abstract
In manufacturing it is common to be required to simultaneously meet several performance measures with varying degrees of conflict among them. Such situation poses a multiple criteria optimization problem. Finding solutions to this kind of problems in an efficient manner is critical for industrial application. In this work, data clustering techniques are explored to make the solution process to multicriteria optimization problems efficient via Data Envelopment Analysis. The results of different clustering schemes are reported and conclusions are drawn from their evaluation.