2025/09/11 by Maryam Sadat Ghasemzadeh, H. M. Dilshad Alam Digonta, Ghasemzadeh, Maryam +5
Computer Science · Decision Sciences · #Advanced Multi-Objective Optimization Algorithms #FOS: Electrical engineering #Probabilistic and Robust Engineering Design #Risk and Portfolio Optimization #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2509.09422
openalex publication_date 2025/09/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.