Development of Decision Support System through Multi-Objective Optimization for Ill-posed Problems

To ensure customer-defined satisfaction, "Kansei" ( emotion) has attracted interest recently in the design of artifacts in today's climate of global competition and short product life cycles. Accordingly, intelligent decision-making through multi-objective optimization ( MOP) has been proposed as an efficient methodology for such human-centered manufacturing. From this viewpoint, in this study, we extend the application of a MOP method referred to as MOON(2R) to an ill-posed problem that involves both qualitative and quantitative performance measures. Additionally, to facilitate portability of the proposed method, especially in multi-disciplinary decision-making environments, we implement the algorithm in an Excel spreadsheet and validate its effectiveness through a case study.