A Comparative Analysis of Robust and Reliable Designs Using the Compromise Decision Support Problem: A Case Study in Hot Rod Rolling Processes

Abstract

Design under uncertainty is a challenging problem, as a systems performance can be highly sensitive to variations in input parameters and model uncertainty. A conventional approach to addressing such problems is robust optimization, which seeks to enhance design performance by reducing sensitivity to uncertainty. Alternatively, reliability-based design focuses on optimizing performance while ensuring that failure constraints are satisfied with a specified probability. While both methods are well established, their integration into multi-objective and multi-stakeholder decision-making frameworks remains a challenging problem. In this study, we extend the Compromise Decision Support Problem (cDSP) framework to incorporate reliability-based design considerations and evaluate its performance in comparison to the conventional robust-based cDSP formulation. The developed framework has been validated on a multidisciplinary hot rod rolling process including parametric and model uncertainties. The results compare the predicted performance under robust and reliable scenarios, validating the efficiency of the approach in managing uncertainties for complex, multidisciplinary systems. Specifically, we found that the two methods exhibit markedly different performance when the predicted performance follows a non-normal distribution, a situation that arises in non-linear systems with parametric uncertainty. Based on this insight, we offer guidance to designers on the conditions under which each method is most appropriate.