Decision-guidance method for knowledge discovery and reuse in multi-goal engineering design problems

When designing complex engineered systems, designers typically encounter different types of complexity in decision-making, such as learning trade-offs among conflicting system goals and concurrently meeting specifications of subsystems while complying with all constraints. Therefore, when managing knowledge of the concurrent design of multi-component systems, one must consider the decision interactions among designers with different interests and the coupling effects of subsystems. Conventional multi-objective optimization methods are inadequate in providing decision support on compromising the achievement of conflicting goals under various design circumstances; they are incompetent to provide knowledge from the supply side. They may also fail to solve the problems endorsed by decision-makers' evolving preferences; that is, they are incompetent in gaining knowledge from the demand side. Enhancing decision support and managing various stakeholders’ preferences is essential for engineering design problems. To address this issue, we propose a method to give systematic guidance for knowledge discovery and reuse in learning the trade-offs among conflicting goals with various preferences. Using the proposed method, a designer can (1) select appropriate design scenarios to compromise the achievement of system goals while maintaining an acceptable level of fidelity and (2) explore and refine the subsystems coupling in a structured and computable manner. We made the contributions above through (1) reusable knowledge identification and ontology development for creating and archiving the knowledge associated with the problem-solving process, (2) implementing interactive decision-making by adopting different guiding strategies, (3) working on satisfying solutions, and (4) developing icon-based knowledge representation and execution of decision workflows in the knowledge-based design guidance system. The whole process forms a loop, namely, the formulation-refinement-exploration-improvement loop. We demonstrate the efficacy of our method using a test problem, the design of a small-scale thermal system that is widely applied in freshwater production and agricultural irrigation.