Identifying Pareto-Optimal Configurations through Integrated Impact and Tradeoff Analysis in System Design

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Abstract
During the design of systems, engineers collaborate across disciplines with the aim of identifying a suitable design that matches their expectations. Such design is composed of a group of components, called a configuration. A configuration is Pareto-optimal if there is no other configuration that improves at least one objective without worsening another. Identifying such configurations is often performed using processes that are separated from one another. This costs time and is error prone during their integration. Despite contributions in this field, engineers are still provided with Pareto-optimal configurations but no means of analyzing their impact across coupled subsystems. It remains difficult for engineers to decide which configuration to select for their design. Moreover, tradeoff analysis that enables the evaluation of alternative configurations is usually performed using separate tools. This increases the burdens for engineers who understand the changes across subsystems, but have to evaluate these changes to find an optimal configuration. To address these challenges, this paper presents Change Impact Analysis Tradeoff (CIAT), an approach that identifies and analyzes configurations dynamically. To achieve this, it integrates knowledge graphs with change propagation, impact analysis, and tradeoff analysis in a single process. These capabilities favor dynamic analysis of configurations that can later be used by a digital twin to support design decisions. The proposed approach is currently illustrated in the context of space system design.

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