## 6 Conclusions  
Generally speaking, the MCDM problems are essential in modern society for businesses and private individuals. The existing MCDM methods, such as GCE and TOPSIS, have some disadvantages which cannot take the interaction of criteria into consideration. In the view of this, a novel MCDM method that combines the GCE, TOPSIS, and fuzzy integral, called the fuzzy grey TOPSIS, was proposed to improve the deficiency mentioned above. The effectiveness of the proposed method was verified by the illustrative example of supplier evaluation of a collaborative manufacturing enterprise as well as some sensitivities. There are some recommendations for future studies. One idea is to focus on other types of fuzzy numbers and correlation coefficients and even redefine them. We will compare them by conducting more experiments and refine the proposed method [85; 86; 87]. As such, hybridizing the proposed MCDM method with recent advances in heuristics and metaheuristics is another good continuation of this work [88; 89; 90; 91; 92; 93]. Conceptualization, G.T. and J.T. ; Methodology, N.H. and Z.L. ; Hardware and software, A.M.F.-F. and Z.Z. ; Writing and validation, Y.F, Z.Z. and W.W. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **P** & **A1** & **A2** & **A3** & **A4** & **A5** & **A6** & **Sequence** \\ \hline CS & 0 & 0.8073 & 0.3274 & 0.3498 & 0.5737 & 0.5000 & 0.5003 & 1,4,6,5,3,2 \\ ACD & 0 & 0.9097 & 0.4677 & 0.5022 & 0.6684 & 0.6907 & 0.6228 & 1,5,4,6,3,2 \\ RCD & 0 & 0.8929 & 0.3807 & 0.3769 & 0.6949 & 0.5286 & 0.5685 & 1,4,6,5,2,3 \\ CS & 0.2 & 0.7588 & 0.3324 & 0.3550 & 0.5718 & 0.5037 & 0.5020 & 1,4,5,6,2,3 \\ ACD & 0.2 & 0.9097 & 0.4677 & 0.5022 & 0.6684 & 0.6907 & 0.6228 & 1,5,4,6,3,2 \\ RCD & 0.2 & 0.8929 & 0.3807 & 0.3769 & 0.6949 & 0.5286 & 0.5685 & 1,4,6,5,2,3 \\ CS & 0.4 & 0.7286 & 0.3368 & 0.3596 & 0.5704 & 0.5077 & 0.5039 & 1,4,5,6,3,2 \\ ACD & 0.4 & 0.9098 & 0.4677 & 0.5022 & 0.6684 & 0.6907 & 0.6228 & 1,5,4,6,3,2 \\ RCD & 0.4 & 0.8929 & 0.3807 & 0.3769 & 0.6949 & 0.5286 & 0.5685 & 1,4,6,5,2,3 \\ CS & 0.6 & 0.7081 & 0.3406 & 0.364 & 0.5692 & 0.5122 & 0.5060 & 1,4,5,6,3,2 \\ ACD & 0.6 & 0.9098 & 0.4677 & 0.5022 & 0.6684 & 0.6907 & 0.6228 & 1,5,4,6,3,2 \\ RCD & 0.6 & 0.8929 & 0.3807 & 0.3769 & 0.6949 & 0.5286 & 0.5685 & 1,4,6,5,2,3 \\ CS & 0.8 & 0.6932 & 0.3440 & 0.3675 & 0.5682 & 0.5172 & 0.5084 & 1,4,5,6,3,2 \\ ACD & 0.8 & 0.9098 & 0.4677 & 0.5022 & 0.6684 & 0.6907 & 0.6228 & 1,5,4,6,3,2 \\ ACD & 0.8 & 0.8929 & 0.3807 & 0.3769 & 0.6949 & 0.5286 & 0.5685 & 1,4,6,5,2,3 \\ RCD & 0.8 & 0.8929 & 0.3807 & 0.3769 & 0.6949 & 0.5286 & 0.5685 & 1,4,6,5,2,3 \\ CS & 1 & 0.6819 & 0.3470 & 0.3708 & 0.5674 & 0.5228 & 0.5112 & 1,4,5,6,3,2 \\ ACD & 1 & 0.9098 & 0.4677 & 0.5022 & 0.6684 & 0.6907 & 0.6228 & 1,5,4,6,3,2 \\ RCD & 1 & 0.8929 & 0.3807 & 0.3769 & 0.6949 & 0.5286 & 0.5685 & 1,4,6,5,2,3 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The result of sensitivity analysis. Figure 8: The result of sensitivity analysis. **Funding:** This work was supported by the Science Fund for Creative Research Groups of National Natural Science Foundation of China (No. 51821093), the National Natural Science Foundation of China (Nos. 51935009 and 51775238), and Zhejiang Provincial Natural Science Foundation of China (No. LZ18E050001). **Acknowledgments:** Sincere appreciation is extended to the reviewers of this paper for their helpful comments. **Conflicts of Interest:** The authors declare no conflict of interest. **Notations and Nomenclature:**