The optimization of complex processes normally involves numerous conflicting objectives. There is typically no solution that provides the user with the best values simultaneously for all criteria. Therefore, the decision-maker needs to decide on a reasonable compromise, and numerous multicriteria optimization methods can assist the decision-maker in performing this task. The method of interest in this study is the Rough Set Method (RSM) where the decision-maker ranks a small subset of Pareto-optimal solutions which serves to encapsulate his preferences in a simple set of preference and non-preference rules that are used to rank the Pareto domain. A new robust RSM is suggested that concentrates on the way the subset of Pareto-optimal solutions is selected and presented to the decision-maker. Three case studies are used to assess the performance of the different variants of RSM. Results show that the improved method is indeed more robust in consistently obtaining a reliable optimum solution. (C) 2009 Elsevier Ltd. All rights reserved.