Selection of pareto-optimal solutions for process optimization using rough set method: A new approach

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.