Product design evaluation based on improved CRITIC and Comprehensive Cloud-TOPSIS – Applied to automotive styling design evaluation

Wang, Zeng; Zhong, Yu; Chai, Shao-long; Niu, Shi-fan; Yang, Ming-lang; Wu, Guo-rong

doi:10.1016/j.aei.2024.102361

Cited by 8 publications

(2 citation statements)

References 45 publications

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“…PDDM involves two critical steps, namely the determination of indicator weights and the ranking of design alternatives [11]. Inspired by the need to address cognitive bias among decision-makers, obtaining objective and reasonable weights forms the bedrock for producing a scientifically sound and dependable outcome of PDDM.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Subsequently, they employ distance algorithms to quantify the dissimilarities between two design alternatives as part of the information aggregation process. For instance, Wang et al [11] transformed qualitative product evaluations from questionnaire into generalized trapezoidal fuzzy numbers and utilized cloud model for ranking product design schemes. Lai et al [12] combined self-reporting, eye-tracking, and electroencephalogram data for the evaluation of product appearance design, and proposed to transfer the probabilistic linguistic term set with interval uncertainty from self-reporting data to a crisp number to facilitate the comparison of automobile appearance design schemes.…”

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: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%