Desirability-based post-hoc selection of Pareto solutions

ABSTRACT

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.

KEYWORDS:

• Desirability
• gradient norm
• optimization
• resilience
• surface
• robustness

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.