Abstract
To tackle the challenge of achieving more reasonably calculation of evaluation indicator weights and a more scientifically rigorous scoring system for ranking in product design evaluation, a novel evaluation model that leverages improved CRITIC (Criteria Improved Criteria Importance) and CC-TOPSIS (Comprehensive Cloud-Technique for Order Preference by Similarity to Ideal Solution) is proposed. Firstly, the process commences with the collection of qualitative product evaluations from respondents through questionnaires, presented in the form of fuzzy confidence intervals. Subsequently, these evaluations are transformed into generalized trapezoidal fuzzy numbers, and their center of gravity values are calculated. Secondly, after the weight for each evaluation indicator is calculated employing an enhanced CRITIC approach, a weight cloud model utilizing the backward cloud methodology is constructed. Subsequently, the scoring cloud model is generated as a backward cloud, followed by the application of an enhanced synthesis operator to amalgamate the weight cloud model with the scoring cloud model, culminating in the formation of a comprehensive cloud model. Finally, the product design schemes are ranked using the Cloud-TOPSIS method, and a cloud chart analysis provides insights into optimization directions. The utilization of this methodology in assessing automotive styling design serves as a compelling showcase of its practicality and efficiency, in addition to, the method's resilience is further bolstered through a sensitivity analysis of the evaluation indicators. This method adeptly computes the weights of evaluation indicators, while also addressing the inherent variability in weight values and incorporates the benefits of Cloud Model and TOPSIS in the evaluation of product design alternatives, ultimately elevating the rationality and the scientific underpinnings of the product design evaluation process.
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