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

Jing, Liting; Fan, Xiaoyan; Feng, Di; Lu, Congda; Jiang, Shaofei

doi:10.1016/j.asoc.2024.111492

Cited by 1 publication

(2 citation statements)

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“…(3) Each proposed design solution is meticulously evaluated against the established indicators; (4) Both indicator and decision-maker weights are computed, reflecting their relative importance and influence in the decision-making process; (5) Harnessing axiomatic design principles, the collective PDDM information from various decision-makers is synthesized into a cohesive analysis; (6) Based on the assessment outcomes, design alternatives are refined. The process then iterates, revisiting steps 1 to 5 as the PDDM enters subsequent stages, each time informed by the refinements and learnings of prior stages; (7) Finally, weights are assigned to each design stage, and the aggregated PDDM insights from all stages are consolidated, ensuring a comprehensive and iterative refinement of product designs over multiple phases.…”

Section: Description Of Heterogeneous Pddm Informationmentioning

confidence: 99%

“…Real numbers, intervals, linguistic terms, and other types of fuzzy numbers [4][5][6] are employed to depict decision-makers' evaluations and the aggregation of heterogeneous PDDM information emerges as a vital process in deriving the optimal design solution. To address the challenge of handling the heterogeneous PDDM information, numerous methodologies have been put forward, which involve heterogeneous information transformation [7], weight determination [8], consensus-reaching [9], and PDDM information aggregation [10]. These studies provide strong support for mitigating cognition bias, bridging knowledge disparities among decision-makers, and fortifying the reliability of obtained PDDM results.…”

Section: Introductionmentioning

confidence: 99%

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Research on Multistage Heterogeneous Information Fusion of Product Design Decision-Making Based on Axiomatic Design

Yang,

Zuo,

Zhang

et al. 2024

Systems

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The product design process, fraught with uncertainties and ambiguities in its requirements and constraints, commonly traverses multiple stages, each emphasizing distinct design aspects. This engenders heterogeneity in decision-making criteria, rendering the effective integration of information from various stages of product design decision-making (PDDM) a pivotal task in identifying the optimal design solution. Surprisingly, limited research has attended to the challenge of consolidating such heterogeneous information across multiple PDDM stages. To bridge this gap, our study employs real numbers, interval numbers, and linguistic terms to capture the heterogeneous judgments of decision-makers. We fuse the Maximization Deviation Method with the analytic hierarchy process (AHP) for determining indicators’ weights, while decision-makers’ weights are derived through a dual consideration of uncertainty measure using fuzzy entropy and a distance-minimization model applied to the PDDM matrix for achieving consistency. Leveraging the advantage of axiomatic design, product design alternatives are evaluated based on their PDDM information content of PDDM matrices. Given the multistage nature of product design, stages’ weights are computed by assessing the information content and consistency degree of PDDM matrices at each stage. Ultimately, our approach achieves multistage heterogeneous decision-making fusion in product design through information axiom weighting. A case study involving the decision-making process for a specific numerical control machine design illustrates the efficacy of our method in integrating multistage heterogeneous PDDM data, yielding a comprehensive perspective on the viability of product design schemes. Results show that the ranking sequence of the product design schemes solidifies to x3 > x2 > x1 in stages 2 and 3 of PDDM, diverging from the initial order observed in stage 1 (x2 > x3 > x1), while the fused result from the multistage heterogeneous PDDM analysis aligns with the later stages’ rankings, indicating the credibility and persuasiveness are fortified. This methodology thus offers a robust framework for synthesizing and navigating the uncertainties and complexities inherent in multistage heterogeneous PDDM contexts.

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Section: Description Of Heterogeneous Pddm Informationmentioning

confidence: 99%