A patent text-based product conceptual design decision-making approach considering the fusion of incomplete evaluation semantic and scheme beliefs

Abstract

Conceptual design evaluation is a significant challenge in product development to select suitable conceptual schemes (CS). Relying on decision makers' (DMs) personal experience for design information may result in incomplete and ambiguous evaluation semantics, creating an incomplete decision matrix. Previous approaches use attributes based on candidate schemes to complete the missing information, but neglect the mining of associated design information from external resources such as patents, reducing the beliefs objectivity after missing semantic completion. Besides, non-compensatory nature between DMs’ interactivity and criteria have not been well considered. To fill these issues, a patent text-based CS decision approach considering the fusion of incomplete semantics and scheme beliefs is proposed. First, using the beneficial effects text from patents as historical design data, constructing general evaluation criteria using the Latent Dirichlet Allocation topic model, and introducing the Apriori model to extract strong association rules between criteria to complete the incomplete semantics. Second, a fuzzy measure model for DMs is constructed based on intuitionistic fuzzy cross-entropy and Shapley value method, supporting the allocation of DMs' interactive weights. Then, the fuzzy Dempster-Shafer evidence theory is used to transform the multi-criteria decision model into a belief fusion problem of CS that avoids aggregating the evaluation semantics of non-compensatory criteria and outputting the optimal CS with interval credibility. A practical case study of an in-pipe inspection robot will be employed to validate the proposed approach, sensitivity analysis and comparison results confirmed that the complement data have the reliability to avoid continuous investment in the evaluation process.