# Evaluation and Classification of Overseas Talents in China Based on the BWM for Intuitionistic Relations

^{*}

^{†}

## Abstract

**:**

## 1. Introduction

## 2. Discussion on Criteria Weight

**Definition**

**1.**

**Definition**

**2.**

**Definition**

**3.**

**Definition**

**4.**

**Definition**

**5.**

- (1)
- ${f}_{{a}_{ij}}+{f}_{{a}_{ij}^{c}}=1$.
- (2)
- It is obvious that, ${G}_{a}^{f}>0$; ${f}_{{a}_{ij}}=\frac{1}{2}$, if ${\rho}_{{a}_{ij}}={\sigma}_{{a}_{ij}}$; ${f}_{{a}_{ij}}>\frac{1}{2}$, if ${\rho}_{{a}_{ij}}>{\sigma}_{{a}_{ij}}$; ${f}_{{a}_{ij}}<\frac{1}{2}$, if ${\rho}_{{a}_{ij}}<{\sigma}_{{a}_{ij}}$; ${G}^{f}$ indicates which kind of preference relation of ${x}_{i}$ over ${x}_{j}$ is dominant, the preferred degree or the non-preferred degree.
- (3)
- Let ${a}^{1}=({\rho}_{{a}^{1}},{\sigma}_{{a}^{1}})$ and ${a}^{2}=({\rho}_{{a}^{2}},{\sigma}_{{a}^{2}})$ be two IMNs. If ${\rho}_{{a}^{1}}\ge {\rho}_{{a}^{2}}$ and ${\sigma}_{{a}^{1}}\le {\sigma}_{{a}^{2}}$, then ${f}_{{a}^{1}}\ge {f}_{{a}^{2}}$.

**Definition**

**6.**

**Definition**

**7.**

**Definition**

**8.**

**Example**

**1.**

## 3. A Non-Linear BWM for an Intuitionistic Relation

#### 3.1. BWM for Intuitionistic Relations

**Definition**

**9.**

**Definition**

**10.**

**Definition**

**11.**

#### 3.2. Model Construction

**Example**

**2.**

#### 3.3. Some Properties about the Non-Linear BWM for Intuitionistic Relations

## 4. The Illustrative Study

## 5. Conclusions and Further Research

- (a)
- The proposed method contains two parts: one is about calculating the weights of criteria and decision makers; the other one is about ranking the alternatives based on the obtained weights. We introduce the fuzz-based geometric index matrix to calculate the consistency degree of decision makers, whose weights can be obtained. Additionally, the criteria’s weights are given subsequently. The importance of decision makers’ weights is obvious, which can also be shown in Example 1. Based on the original BWM, we develop it into the intuitionistic preference relation’s environment and construct a non-linear BWM. Then, the decision process can be more effective than the other pairwise-based decision making method.
- (b)
- This paper studies the overseas talents’ evaluation and classification problem of China. After summarizing the published related references and analyzing the Chinese context, we construct a criteria table for evaluation. Then, we apply the proposed method to rank 20 overseas talents and classify them. According to the specificity of human resource evaluation, we add the comparison of non-preferred degree by introducing the intuitionistic preference relation. This extension is more suitable for human’s decision making psychology, leading to the decision making process being closer to reality. Additionally, it also can decrease the subjective influence on the decision results. Research studies on the overseas talents of China are rare, and the published papers do not consider the situation as extensively as this paper dose. Therefore, this paper is meaningful for the human resources practices system.
- (c)
- We transform the talents evaluation problem into a Multi-Criteria Group Decision Making (MCGDM) problem, and solve it through a combination MCGDM methodology. For this proposed methodology, we develop the related methods to make them more effective. Example 1 can show the importance of considering geometry consistency degree, rather than paying more attention to calculating the missing elements in the reference of [44]. The illustrative study demonstrates the applicable of the proposed model. We also give two properties to measure whether the decisions are within the reasonable range. That ensures the effectiveness and reasonableness of our methodology.

- (a)
- The proposed method is based on intuitionistic preference relations. However, in some more complicated conditions, this tool may still be insufficient to describe decision makers’ hesitation degree. Decision making of alternatives with incomplete information is not a focus in this paper. As the decision problem in reality becomes gradually more complicated, it is unavoidable to deal with the complex problems that decision making results may be incomplete.
- (b)
- The method is suitable for the overseas talent evaluation and classification problem, whose number of criteria and alternatives is moderate. Compared with the general pairwise comparison methods, we improve the practicability by introducing BWM. However, faced with large numbers of alternatives, our method may have heavy workloads and high cost.
- (c)
- During the step of calculating criteria and decision makers’ weights, we consider the geometry consistency degree. The consistency of BWM with intuitionistic preference relations has not been considered in this paper. This consistency degree may bring uncertain influence on the ranking results.

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

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${\mathit{\alpha}}_{{\mathit{x}}_{\mathit{B}\mathit{W}}}$ | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
---|---|---|---|---|---|---|---|---|---|

ξ | 0 | 0.4384 | 1.0000 | 1.6277 | 2.2984 | 3.0000 | 3.7251 | 4.4689 | 5.2280 |

${\mathit{\beta}}_{{\mathit{x}}_{\mathit{B}\mathit{W}}}$ | 1 | 1/2 | 1/3 | 1/4 | 1/5 | 1/6 | 1/7 | 1/8 | 1/9 |
---|---|---|---|---|---|---|---|---|---|

η | 0 | 2.1180 | 1.7908 | 1.6160 | 1.5062 | 1.4304 | 1.3747 | 1.3321 | 1.2519 |

C_{1} | C_{2} | C_{3} | C_{4} | |
---|---|---|---|---|

(${x}_{B1}$,${x}_{1W}$) | ((9,$\frac{1}{9}$),(1,1)) | ((8,$\frac{1}{8}$),(1,1)) | ((6,$\frac{1}{7}$),(1,1)) | ((7,$\frac{1}{8}$),(1,1)) |

(${x}_{B2}$,${x}_{2W}$) | ((3,$\frac{1}{4}$),(4,$\frac{1}{5}$)) | ((3,$\frac{1}{4}$),(5,$\frac{1}{5}$)) | ((2,$\frac{1}{3}$),(4,$\frac{1}{5}$)) | ((3,$\frac{1}{5}$),(4,$\frac{1}{4}$)) |

(${x}_{B3}$,${x}_{3W}$) | ((1,1),(9,$\frac{1}{9}$)) | ((1,1),(8,$\frac{1}{8}$)) | ((1,1),(6,$\frac{1}{7}$)) | ((1,1),(7,$\frac{1}{8}$)) |

$\mathbf{(}{\mathit{C}}_{\mathbf{1}}\mathbf{\left(}\mathit{\alpha}\mathbf{\right)}\mathbf{,}{\mathit{C}}_{\mathbf{1}}\mathbf{\left(}\mathit{\beta}\mathbf{\right)}\mathbf{)}$ | $\mathbf{(}{\mathit{C}}_{\mathbf{2}}\mathbf{\left(}\mathit{\alpha}\mathbf{\right)}\mathbf{,}{\mathit{C}}_{\mathbf{2}}\mathbf{\left(}\mathit{\beta}\mathbf{\right)}\mathbf{)}$ | $\mathbf{(}{\mathit{C}}_{\mathbf{3}}\mathbf{\left(}\mathit{\alpha}\mathbf{\right)}\mathbf{,}{\mathit{C}}_{\mathbf{3}}\mathbf{\left(}\mathit{\beta}\mathbf{\right)}\mathbf{)}$ | $\mathbf{(}{\mathit{C}}_{\mathbf{4}}\mathbf{\left(}\mathit{\alpha}\mathbf{\right)}\mathbf{,}{\mathit{C}}_{\mathbf{4}}\mathbf{\left(}\mathit{\beta}\mathbf{\right)}\mathbf{)}$ | $\mathbf{(}{\mathit{C}}_{\mathit{w}}\mathbf{\left(}\mathit{\alpha}\mathbf{\right)}\mathbf{,}{\mathit{C}}_{\mathit{w}}\mathbf{\left(}\mathit{\beta}\mathbf{\right)}\mathbf{)}$ | |
---|---|---|---|---|---|

${x}_{1}$ | (0.071,0.051) | (0.071,0.055) | (0.091,0.064) | (0.084,0.057) | (0.076,0.055) |

${x}_{2}$ | (0.258,0.214) | (0.295,0.218) | (0.336,0.259) | (0.276,0.189) | (0.287,0.222) |

${x}_{3}$ | (0.671,0.735) | (0.633,0.727) | (0.573,0.677) | (0.640,0.754) | (0.637,0.723) |

${\xi}^{*}({C}_{i})$ | 0.394 | 0.860 | 0.298 | 0.683 | - |

${\eta}^{*}({C}_{i})$ | 0.041 | 0.050 | 0.048 | 0.050 | - |

Criteria | Description |
---|---|

Basic quality | Age, physical condition, psychological health status and credit standing. |

Capacity | The benefits created for the organization, optimization of industry output and promotion of the industrial and social development. |

Contributions | The benefits created for the organization, optimization of industry output and promotion of the industrial and social development. |

Development potential | Innovations and innovation ability, enterprise, ability to solve difficulties and resist setback. |

Internationalization | Global perspective and cross-cultural adaptability. |

C_{1} | C_{2} | C_{3} | C_{4} | C_{5} | Weighted | |
---|---|---|---|---|---|---|

$D{M}_{1}$ | (0.4722,0.0557) | (0.4963,0.0704) | (0.5274,0.0725) | (0.5954,0.0704) | (0.4963,0.0593) | (0.5164,0.0699) |

$D{M}_{2}$ | (0.5274,0.0781) | (0.4906,0.0887) | (0.4041,0.0784) | (0.5730,0.0648) | (0.4861,0.0595) | (0.4977,0.0765) |

$D{M}_{3}$ | (0.4121,0.0409) | (0.4079,0.0789) | (0.4121,0.0550) | (0.4068,0.5274) | (0.5274,0.0601) | (0.4280,0.0624) |

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## Share and Cite

**MDPI and ACS Style**

Yang, Q.; Zhang, Z.; You, X.; Chen, T.
Evaluation and Classification of Overseas Talents in China Based on the BWM for Intuitionistic Relations. *Symmetry* **2016**, *8*, 137.
https://doi.org/10.3390/sym8110137

**AMA Style**

Yang Q, Zhang Z, You X, Chen T.
Evaluation and Classification of Overseas Talents in China Based on the BWM for Intuitionistic Relations. *Symmetry*. 2016; 8(11):137.
https://doi.org/10.3390/sym8110137

**Chicago/Turabian Style**

Yang, Qing, Zaisheng Zhang, Xinshang You, and Tong Chen.
2016. "Evaluation and Classification of Overseas Talents in China Based on the BWM for Intuitionistic Relations" *Symmetry* 8, no. 11: 137.
https://doi.org/10.3390/sym8110137