Guo, LinMilisavljevic-Syed, JelenaWang, RuHuang, YuAllen, Janet K.Mistree, Farrokh2022-09-292022-09-292022-09-25Guo L, Milisavljevic-Syed J, Wang R, et al., (2023) Managing multi-goal design problems using adaptive leveling-weighting-clustering algorithm, Research in Engineering Design,Volume 34, Issue 1, January 2023, pp. 39–600934-9839https://doi.org/10.1007/s00163-022-00394-zhttps://dspace.lib.cranfield.ac.uk/handle/1826/18493In this paper, we address the issue of solving problems with multiple components, multiple objectives, and target values for each objective. There are limitations in managing these multi-component, multi-goal problems such as the need for domain expertise to combine or prioritize the goals. In this paper, we propose a domain-independent method, Adaptive Leveling-Weighting-Clustering (ALWC), to manage the exploration of design scenarios of multi-goal, engineering-design problems. Using ALWC, designers explore combinations and priorities of the goals based on their interrelationships. Through iteration, design scenarios are obtained with higher goal achievements and an improved understanding of the relationship among subsystems. This is achieved without increasing computational complexity. This knowledge is helpful for multi-component design. The ALWC method is demonstrated using a thermal-system design problem.enAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/Multi-goal problemsCompromise decision support problemsAdaptive Leveling-Weighting-Clustering (ALWC) methodClustering analysisArticle1435-6066