Desirability-based post-hoc selection of Pareto solutions.

Generating a set of optimal solutions is a recommended practice when solving a multiresponse problem. However, it is known that some optimal solutions may yield unexpected outcomes when implemented in practice. Thus, to avoid wasting resources and time in implementing a theoretical optimal solution that does not produce the expected outcomes, a new approach to select a solution from the Pareto front is proposed. This approach employs a desirability-based function to aggregate all the desired response characteristics, namely the responses' Bias, Resilience, Quality of Predictions, and Robustness. Two case studies illustrate the usefulness of the proposed approach. [ABSTRACT FROM AUTHOR]