Information-intensive design solution evaluator combined with multiple design and preference information in product design

Abstract To assist designers in making comprehensive decisions for objective design values (DVs) and subjective preference values (PVs) during the design solution evaluation stage, this study builds an information-intensive design solution evaluator (IIDSE) that combines multi-information from DVs and PVs. In the IIDSE, the importance degrees of the DVs and PVs are analysed based on their differences. Then, according to the importance classifications, values, characteristics, and numbers of DVs and PVs, a multi-information fusion (MIF)-based ideal solution definition strategy, which covers quantitative criteria with i) benefit characteristics, ii) cost characteristics, and iii) qualitative criteria, is proposed. A rough multi-criteria decision-making (R-MCDM) model is used to evaluate an alternative by computing its deviation from the defined ideal solution. The effectiveness of the IIDSE was validated via empirical comparisons. Experiment I showed that the MIF-based strategy is compatible with different R-MCDM models for selecting the preferred and best performing solution. In experiment II, among the R-MCDM models, R-COPRAS plus the MIF-based strategy is the best combination for constructing the IIDSE. Experiments III and IV demonstrated that the IIDSE can obtain more reasonable solutions compared with classical evaluators, especially in the case where conflictions between the objective DVs and subjective PVs exist.