## 2 Literature Review  
Academically, many studies have been conducted on MCDM problems, and substantial progress has been made in this field. For example, [PERSON] et al. formulated a consensus bipolar method to solve the collaborative group MCDM problems considering the impact of human behavior, e.g., individualism, fear, caution [42]. The analysis results of the application example about real size wind farm implantation problem showed that this proposed method can be a useful decision-making tool to solve this problem [42]. [PERSON] et al. proposed a bipolar aggregation method that combines weighted cardinal fuzzy measures, which can effectively overcome difficulties that dissuade the use of Choquet integral in practices to solve the fuzzy nominal classification in the MCDM problem [43]. [PERSON] et al. formulated a hybrid optimization approach combining the best worst method, grey relational analysis, and visekriterijumsko kompromisno rangiranje (VIKOR) to solve the MCDM problem in rail transit [44]. A multi-cell thin-walled aluminum energy-absorbing structure was applied to verify that this integrated method is valid and practical [44]. The results proved that this method provides an accurate and effective tool for the structural decision-making problem in rail transit [44]. [PERSON] and [PERSON] presented a new multi-criteria decision-making approach for the sustainable material selection problem [45]. Ten examples were applied to verify this proposed method [45]. TOPSIS and GCE are two of the several important methods in dealing with MCDM problems. Many researchers have a contribution to hybridization of MCDM techniques with some other method  
Figure 2: Grey comprehensive evaluation (GCE) failed conditions. to make the best use of their advantages and bypass their disadvantages, as shown in Tables 1 and 2. In addition, the fuzzy integral and fuzzy set are used to address the interaction among criteria which have been applied to many domains, as exposed in Table 3. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Typical Reference** & **Tool Type** & **Advantage** & **Limitation** \\ \hline [PERSON] et al. [46] & TOPSIS & Flexible framework & Experts have too \\  & & & many rights \\ \hline [PERSON] et al. [47] & Fuzzy TOPSIS & Simple & Hard to describe \\  & & & complex systems \\ \hline [PERSON] et al. [48] & TOPSIS and COPRAS, SAW & Various comparison & Hard to describe \\  & & & complex systems \\ \hline [PERSON] et al. [49] & Fuzzy TOPSIS and Fuzzy-AHP & More realistic and reliable & Complicated and \\  & & & abstract models \\ \hline [PERSON] et al. [50] & Fuzzy TOPSIS and Fuzzy-AHP & More realistic and reliable & Complicated and \\  & & abstract models \\ \hline [PERSON] and [PERSON] [51] & Ordinary and Block TOPSIS & Simple and fast & Complicated \\ \hline [PERSON] [52] & TOPSIS & Simple and fast & Not accurate enough \\ \hline [PERSON] et al. [53] & Weighted Euclidean distances & Simple and direct & Not accurate enough \\  & TOPSIS & & \\ \hline [PERSON] and [PERSON] [54; 55] & Fuzzy AHP and fuzzy TOPSIS & Solving the uncertainty & Hard to describe \\  & & problem of score evaluation & complex systems \\ \hline [PERSON] et al. [56; 57; 58] & TOPSIS, COPRAS, and ARAS & Aggregate both quantitative & Complicated and \\  & & and qualitative criteria & abstract models \\ \hline [PERSON] and [PERSON] [59] & Ashby, VIKOR, and TOPSIS & Various comparison & Complicated and \\  & & abstract models \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Review of the technique for order preference by similarity to an ideal solution (TOPSIS).