# Conceptual Design Evaluation Considering Confidence Based on Z-AHP-TOPSIS Method
## 5 Discussion  
It can be concluded from Table 7 that design 2 is the best scheme. Because designers are more confident in their evaluation, the closeness of design 2 is greater than that of the other three designs, which shows that if designers are more confident in evaluation, it is easier to get the best design, and the impact of confidence cannot be ignored. In Table 10, design 2 is also the best scheme, but because the managers are less confident than the designers, the closeness of the four schemes will be closer, so hesitation is not conducive to attaining the best scheme. It is worth noting that when hesitant, we can optimize design 2 through the confident content of other designs. For example, the convenience evaluation value of design 2 is good, and the confidence level is likely. In contrast, the convenience evaluation value of design 3 is very good, and the confidence level is absolutely. It is evident that design 3 is more convenient than design 2, so design 2 can be optimized by taking advantage of the advantages of design 3.  
Design 3 adopts a side opening to improve convenience, so the side opening of design 3 is integrated into design 2, and then the original design is simplified and the runner at the bottom is removed to reduce complexity. The rendering of the final design is shown in Figure 10.  
As TOPSIS [44], fuzzy TOPSIS [45], AHP [46], and fuzzy AHP [47] do not consider the influence of self-confidence on the scheme, it can be seen from Table 10 that Design 3 = Design 4 when using these methods, because their evaluation values are the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Design Possibility** & \(C_{i}\) & **Rank** \\ \hline Design 1 & 0.5319 & 2 \\ Design 2 & 0.5449 & 1 \\ Design 3 & 0.5253 & 4 \\ Design 4 & 0.5316 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Ranking of the design possibility (managers).  
Figure 10: Final conceptual design.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Criterion \(\rightarrow\)** & \(C_{1}\) & \(C_{2}\) & \(C_{3}\) & \(C_{4}\) & \(C_{5}\) \\
**Alternative \(\downarrow\)** & \(w_{1}\) = 0.41 & \(w_{2}\) = 0.35 & \(w_{3}\) = 0.17 & \(w_{4}\) = 0.05 & \(w_{5}\) = 0.02 \\ \hline Design 1 & (P, W) & (F, N) & (G, A) & (G, A) & (G, A) \\ Design 2 & (E, A) & (V, A) & (F, A) & (E, A) & (P, A) \\ Design 3 & (F, W) & (F, W) & (V, N) & (F, W) & (G, W) \\ Design 4 & (P, N) & (G, N) & (V, A) & (V, A) & (V, A) \\ \hline \hline \end{tabular}
\end{table}
Table 9: Decision matrix of managers.  
same, but the influence of self-confidence cannot be ignored, so Design 4 > Design 3 in the actual situation. The same problem may occur when determining the weight of criteria.  
In conceptual design evaluation, the hybrid fuzzy method is relatively safe [39; 48]. Considering that with the increasing complexity of products more DMs may participate in the evaluation, ignoring the confidence may bring potential risks caused by their experience and knowledge differences.  
According to the self-confidence, it is easier to determine the factors of customer needs and the advantages of the design. At the same time, it can improve the disadvantages of the design through other schemes. Therefore, in the conceptual design, the method proposed in this paper has more advantages than the method without considering the self-confidence.