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A Hybrid Decision Making Framework for Personnel Selection Using BWM, MABAC and PROMETHEE

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Abstract

Personnel selection plays a vital role in the sustainable development of a company. Generally, both quantitative and qualitative criteria are considered in the personnel selection process. Hence, this research introduces crisp numbers and linguistic neutrosophic numbers (LNNs) simultaneously to express hybrid evaluation information. Then, the multi-attributive border approximation area comparison (MABAC) method is recommended to select ideal personnel because of its simplicity and precision. Some criteria have the feature of non-compensation in real personnel selection, but they are presumed to be compensatory in MABAC. To overcome this limitation, the idea of preference ranking organization method for enrichment evaluations (PROMETHEE) is integrated into MABAC. Besides, the traditional best–worst method (BWM) is modified with linguistic values to obtain the criteria weights more appropriately. As a result, a hybrid decision making framework is constructed to tackle personnel selection issues. Finally, an illustrative example of personnel selection in an IT company is given to show the procedures of the proposed method after the assessment criteria system is built. Moreover, some comparative analyses are made to justify the practicability and strengths of our method. Results demonstrate that the hybrid decision making framework is eligible and helpful for personnel selection in enterprises.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 61773120).

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  1. College of Systems Engineering, National University of Defense Technology, Changsha, 410073, People’s Republic of China

    Sui-zhi Luo & Li-ning Xing

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  1. Sui-zhi Luo
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Correspondence to Li-ning Xing.

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Luo, Sz., Xing, Ln. A Hybrid Decision Making Framework for Personnel Selection Using BWM, MABAC and PROMETHEE. Int. J. Fuzzy Syst. 21, 2421–2434 (2019). https://doi.org/10.1007/s40815-019-00745-4

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