Conceptual Design Evaluation Considering Confidence Based on Z-AHP-TOPSIS Method

Abstract

In concept design, effective decision making and management of schemes can shorten the design cycle and improve product quality. The decision maker (DM)’s confidence is one of the critical factors affecting the conceptual design evaluation. Although many studies use quantitative linguistic evaluation for design scheme decision-making, which improves product conceptual design decision-making efficiency and effectiveness, few studies consider the confidence level of a decision. A conceptual design evaluation method based on Z-numbers is proposed to solve this problem, considering the customer requirements and the DM’s confidence. Firstly, the evaluation criteria are determined by analyzing customer requirements; then, the fuzzy analytic hierarchy process in the Z-numbers environment (Z-AHP) is used to determine the criteria weight; Finally, the fuzzy technique for order preference by similarity to ideal solution method in the Z-numbers environment (Z-TOPSIS) is used to evaluate the design schemes to obtain the optimal scheme. The proposed method is applied to the selection of the design scheme of the waste containers in the kitchen. The results show that considering the DM’s self-confidence can achieve a more reasonable and practical evaluation of the conceptual design scheme, and it is easier to obtain the best scheme.

Evaluation of product conceptual design based on Pythagorean fuzzy set under big data environment

“…Although the methods of concept design evaluation have been continuously optimized, the evaluation criteria used in these studies are still mainly based on traditional survey methods such as expert opinion 34 , 36 , 44 , literature review 4 , 34 , 37 , 41 and questionnaires 18 , 35 , which are feasible but have obvious drawbacks such as time-consuming, slow feedback, low user involvement, and small research These methods are feasible but have obvious drawbacks, such as time-consuming, slow feedback, low user participation, and small scope. A prerequisite for effective methods to obtain accurate and objective product concept evaluation results is the establishment of comprehensive and objective evaluation criteria.…”

“…3 and their weight values (Table 6 ) to apply the PFTOPSIS model to the conceptual design of kitchen waste containers discussed by Liu et al 18 . Ten participants were appointed randomly to form a decision panel to express their viewpoints on the conceptual design options in linguistic terms (Table 7 ) after learning about the four conceptual design options for kitchen waste containers shown by Liu et al 18 . Table 10 presents the collated decision matrix, Table 11 shows the corresponding Pythagorean fuzzy PIS and NIS, and Table 12 provides the distances of the conceptual design solutions from the Pythagorean fuzzy PIS and NIS, along with the revised closeness of the conceptual design solutions and the final ranking of the solutions.…”

“…For example, if a rating of 5 is meant to be “superior”, then a rating of 5 is “excellent” and a rating of 4.9 is “not superior”, but in reality, in the user’s understanding, a rating of 5 is not very different from a rating of 4.9. In practice, there is no big gap between 5 and 4.9 in users’ understanding 18 . Pythagorean fuzzy sets can be used to depict linguistic variables and express fuzziness, and integrating Pythagorean fuzzy sets into AHP-TOPSIS can enhance the objectivity of product concept design evaluation results.…”

“…Technically, assessment criteria can be summarized based on expert experience, literature review 4 , and questionnaires 18 , however, the use of generic data will further increase the uncertainty and imprecision of assessment results. With the further development of social media, wearable devices, and smart manufacturing, massive quantities of databases are coming from all directions.…”

Research on the Design of Surgical Auxiliary Equipment Based on AHP, QFD, and PUGH Decision Matrix

“…Some scholars have also applied different quantitative methods to product innovation design or product evaluation in the last two years. Oey et al [23] sought a way to accommodate user requirements in their product and process improvement, Lu et al [24] constructed a product form evolution design method integrating the TRIZ contradiction matrix to simplify the product form evolution process, Liu et al [25] proposed a conceptual design evaluation method based on Z-numbers, Yue et al [26] improved the evaluation system of the design of household medical products for the elderly, Zhang et al [27] proposed a lead user identification method based on user behavior data and contribution content analysis, Wang et al [28] proposed a product evaluation method that combines natural language processing techniques and fuzzy multicriteria decision-making, and Wurster et al [29] used consumers as a valuable source of information to specify features of the output of an innovative CE ecosystem. e overall comparison between this article and previous studies is shown in Table 1.…”