Abstract
For corporates, the performance evaluation of human resources is always a significant strategic activity based on cognitive information. This study aims to develop a novel decision-making method with the computation of cognitive information to make assessments of human resources. First, the cognitive information is described by means of linguistic neutrosophic numbers (LNNs) to capture aspects of indeterminacy and fuzziness. Then, as the Maclaurin symmetric mean (MSM) operators can reflect the interrelations among multiple inputs, several extended MSM operators are proposed to aggregate cognitive information in the linguistic neutrosophic environments. Meanwhile, some important properties of these operators are justified. Thereafter, a linguistic neutrosophic decision-making method based on MSM operators is introduced to address qualitative evaluation problems during cognitive processes. Finally, the validity of our method is revealed by presenting a case study of selecting the best employee in a company. Moreover, the advantages of the proposed method are highlighted by the discussion of the effect of the parameter existing in aggregation operators and the comparison with other methods. The results show that the proposed method is feasible and the study can provide guidelines for the performance evaluation and management of human resources. The utilization of LNNs enriches the expression of cognitive information. Furthermore, the proposed method can be regarded as a potential choice for disposing of cognitive computation.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Garg A, Vijayaraghavan V, Zhang J, Lam JSL. Robust model design for evaluation of power characteristics of the cleaner energy system. Renew Energ. 2017;112:302–13.
Huang YH, Gao L, Yi Z, Tai K, Kalita P, Prapainainar P, Garg A. An application of evolutionary system identification algorithm in modelling of energy production system. Measurement. 2018;114:122–31.
Luo SZ, Liang WZ, Zhao GY. Hybrid PSO-WDBA method for the site selection of tailings pond. Comput Ind Eng. 2020;106429. https://doi.org/10.1016/j.cie.2020.106429.
Catrini P, Piacentino A, Cardona F, Ciulla G. Exergoeconomic analysis as support in decision-making for the design and operation of multiple chiller systems in air conditioning applications. Energ Convers Manage. 2020;220.
Efe B, Kurt M. A systematic approach for an application of personnel selection in assembly line balancing problem. Int T Oper Res. 2018;25(3):1001–25.
Polychroniou PV, Giannikos I. A fuzzy multi-criteria decision-making methodology for selection of human resources in a Greek private bank. Career Dev Int. 2009;14(4):372–87.
Wright PM, Mcmahan GC. Exploring human capital: putting ‘human’ back into strategic human resource management. Hum Resource Manag J. 2011;21(2):93–104.
Luo SZ, Xing LN. A hybrid decision making framework for personnel selection using BWM, MABAC and PROMETHEE. Int J Fuzzy Syst. 2019;21(8):2421–34.
Ozdemir Y, Nalbant KG. Personnel selection for promotion using an integrated consistent fuzzy preference relations-fuzzy analytic hierarchy process methodology: a real case study. Asian J Interdiscipl Res. 2020;3(1):219–36.
Morente-Molinera JA, Kou G, Samuylov K, Ureña R, Herrera-Viedma E. Carrying out consensual group decision making processes under social networks using sentiment analysis over comparative expressions. Knowl-Based Syst. 2019;165:335–45.
Luo SZ, Liang WZ, Zhao GY. Likelihood-based hybrid ORESTE method for evaluating the thermal comfort in underground mines. Appl Soft Comput. 2020;87.
Liang WZ, Zhao GY, Wu H, Chen Y. Optimization of mining method in subsea deep gold mines: a case study. T Nonferr Metal Soc. 2019;29(10):2160–9.
Park JH, Gwak MG, Kwun YC. Linguistic harmonic mean operators and their applications to group decision making. Int J Adv Manuf Tech. 2011;57(1–4):411–9.
Dong YC, Li HY. On consistency measures of linguistic preference relations. Eur J Oper Res. 2008;189(2):430–44.
Wu ZB, Chen YH. The maximizing deviation method for group multiple attribute decision making under linguistic environment. Fuzzy Sets Syst. 2007;158(14):1608–17.
Ju YB. A new method for multiple criteria group decision making with incomplete weight information under linguistic environment. Appl Math Model. 2014;38(21–22):5256–68.
Luo SZ, Zhang HY, Wang JQ, Li L. Group decision-making approach for evaluating the sustainability of constructed wetlands with probabilistic linguistic preference relations. J Oper Res Soc. 2019;70(12):2039–2055.
Gao J, Guo F, Ma Z, Huang X, Li X. Multi-criteria group decision-making framework for offshore wind farm site selection based on the intuitionistic linguistic aggregation operators. Energy. 2020;117899. https://doi.org/10.1016/j.energy.2020.117899.
Chen ZC, Liu PH, Pei Z. An approach to multiple attribute group decision making based on linguistic intuitionistic fuzzy numbers. Int J Comput Int Sys. 2015;8(4):747–60.
Tang M, Liao H. Managing information measures for hesitant fuzzy linguistic term sets and their applications in designing clustering algorithms. Inform Fusion. 2019;50:30–42.
Zhang XY, Xu ZS, Liao HC. A consensus process for group decision making with probabilistic linguistic preference relations. Inform Sciences. 2017;414:260–75.
Tian ZP, Wang J, Zhang HY, Chen XH, Wang JQ. Simplified neutrosophic linguistic normalized weighted Bonferroni mean operator and its application to multi-criteria decision-making problems. Filomat. 2016;30(12):3339–60.
Fang ZB, Ye J. Multiple attribute group decision-making method based on linguistic neutrosophic numbers. Symmetry. 2017;9(7). https://doi.org/10.3390/sym9070111.
Liang WZ, Zhao GY, Hong CS. Performance assessment of circular economy for phosphorus chemical firms based on VIKOR-QUALIFLEX method. Journal of Cleaner Production. 2018;196:1365–78.
Fan CX, Ye J, Hu KL, Fan E. Bonferroni mean operators of linguistic neutrosophic numbers and their multiple attribute group decision-making methods. Information. 2017;8(3). https://doi.org/10.3390/info8030107.
Liu PD, You XL. Some linguistic neutrosophic Hamy mean operators and their application to multi-attribute group decision making, Plos One. 2018;13(3). https://doi.org/10.1371/journal.pone.0193027.
Liang WZ, Zhao GY, Hong CS. Selecting the optimal mining method with extended multi-objective optimization by ratio analysis plus the full multiplicative form (MULTIMOORA) approach. Neural Comput Appl. 2019;31(10):5871–86.
Fan C, Fan E, Hu K. New form of single valued neutrosophic uncertain linguistic variables aggregation operators for decision-making. Cogn Syst Res. 2018;52:1045–55.
Xu ZS, Yager RR. Power-geometric operators and their use in group decision making. IEEE T Fuzzy Syst. 2010;18(1):94–105.
Yang L, Li B. Multiple-valued picture fuzzy linguistic set based on generalized Heronian mean operators and their applications in multiple attribute decision making. IEEE Access. 2020;8:86272–95.
Qin Y, Qi Q, Scott PJ, Jiang X. An additive manufacturing process selection approach based on fuzzy Archimedean weighted power Bonferroni aggregation operators. Robot Cim-Int Manuf. 2020;64.
Maclaurin C. A second letter to Martin Folkes, Esq; concerning the roots of equations, with demonstration of other rules of algebra. Philos T R Soc A. 1729;36:59–96.
Şahin R, Küçük GD. A novel group decision-making method based on linguistic neutrosophic Maclaurin symmetric mean (Revision IV). Cogn Comput. 2020;12(3):699–717.
Qin JD, Liu XW. An approach to intuitionistic fuzzy multiple attribute decision making based on Maclaurin symmetric mean operators. J Intell Fuzzy Syst. 2014;27(5):2177–90.
Ju YB, Liu XY, Ju DW. Some new intuitionistic linguistic aggregation operators based on Maclaurin symmetric mean and their applications to multiple attribute group decision making. Soft Comput. 2016;20(11):4521–48.
Liu PD, Qin XY. Maclaurin symmetric mean operators of linguistic intuitionistic fuzzy numbers and their application to multiple-attribute decision-making. J Exp Theor Artif In. 2017;29(6):1173–202.
Yu SM, Zhang HY, Wang JQ. Hesitant fuzzy linguistic Maclaurin symmetric mean operators and their applications to multi-criteria decision-making problem. Int J Intell Syst. 2018;33(5):953–82.
Xu ZS. Deviation measures of linguistic preference relations in group decision making. Omega. 2005;33(3):249–54.
Zhang SF, Liu SY. A GRA-based intuitionistic fuzzy multi-criteria group decision making method for personnel selection. Expert Syst Appl. 2011;38(9):11401–5.
Manoharan TR, Muralidharan C, Deshmukh SG. An integrated fuzzy multi-attribute decision-making model for employees’ performance appraisal. Int J Hum Resour Man. 2011;22(03):722–45.
Liu HC, Qin JT, Mao LX, Zhang ZY. Personnel Selection using interval 2-tuple linguistic VIKOR method. Hum Factors Ergon Manuf Serv Ind. 2015;25(3):370–84.
Funding
This work is supported by the National Natural Science Foundation of China (No. 61773120, 71331008 and U1501254). This work is also supported in part by the Innovation Team of Guangdong Provincial Department of Education (2018KCXTD031) and the Hunan Provincial Innovation Foundation For Postgraduate (CX20200585).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Ethical Approval
This article does not contain any studies with human participants performed by any of the authors.
Conflict of Interest
The authors declare no competing interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendices
Appendix A
Proof 3
(1) According to Theorem 1
, \(LNMS{M}^{(s)}({A}_{1},{A}_{2},...,{A}_{m})=LNMS{M}^{(s)}(A,A,...,A)\)
(2) As \(0\le {l}_{T{a}_{i}}\le {l}_{T{b}_{i}}\) \(\Rightarrow\) \(T{a}_{i}\le T{b}_{i}\) \(\Rightarrow\) \(\frac{{\prod }_{j=1}^{s}T{a}_{{i}_{j}}}{{(2v)}^{s}}\le \frac{{\prod }_{j=1}^{s}T{b}_{{i}_{j}}}{{(2v)}^{s}}\) \(\Rightarrow 1-\frac{{\prod }_{j=1}^{s}T{a}_{{i}_{j}}}{{(2v)}^{s}}\ge 1-\frac{{\prod }_{j=1}^{s}T{b}_{{i}_{j}}}{{(2v)}^{s}}\)
Then \({l}_{2v{\left(1-{\left({\prod }_{1\le {i}_{1}<\cdots <{i}_{s}\le m}(1-\frac{{\prod }_{j=1}^{s}T{a}_{{i}_{j}}}{{(2v)}^{s}})\right)}^{\frac{1}{{C}_{m}^{s}}}\right)}^\frac{1}{s}}\le {l}_{2v{\left(1-{\left({\prod }_{1\le {i}_{1}<\cdots <{i}_{s}\le m}(1-\frac{{\prod }_{j=1}^{s}T{b}_{{i}_{j}}}{{(2v)}^{s}})\right)}^{\frac{1}{{C}_{m}^{s}}}\right)}^\frac{1}{s}}\).
Additionally, \({l}_{I{a}_{i}}\ge {l}_{I{b}_{i}}\ge 0\) \(\Rightarrow\) \(I{a}_{i}\ge I{b}_{i}\) \(\Rightarrow\) \(\frac{I{a}_{{i}_{j}}}{2v}\ge \frac{I{b}_{{i}_{j}}}{2v}\) \(\Rightarrow\) \({\prod }_{j=1}^{s}(1-\frac{I{a}_{{i}_{j}}}{2v})\le {\prod }_{j=1}^{s}(1-\frac{I{b}_{{i}_{j}}}{2v})\)
then \({l}_{2v-2v{\left(1-{\left({\prod }_{1\le {i}_{1}<\cdots <{i}_{s}\le m}(1-{\prod }_{j=1}^{s}(1-\frac{I{a}_{{i}_{j}}}{2v}))\right)}^{\frac{1}{{C}_{m}^{s}}}\right)}^\frac{1}{s}}\ge {l}_{2v-2v{\left(1-{\left({\prod }_{1\le {i}_{1}<\cdots <{i}_{s}\le m}(1-{\prod }_{j=1}^{s}(1-\frac{I{b}_{{i}_{j}}}{2v}))\right)}^{\frac{1}{{C}_{m}^{s}}}\right)}^\frac{1}{s}}\).
Similarly,\({l}_{2v-2v{\left(1-{\left({\prod }_{1\le {i}_{1}<\cdots <{i}_{s}\le m}(1-{\prod }_{j=1}^{s}(1-\frac{F{a}_{{i}_{j}}}{2v}))\right)}^{\frac{1}{{C}_{m}^{s}}}\right)}^\frac{1}{s}}\ge {l}_{2v-2v{\left(1-{\left({\prod }_{1\le {i}_{1}<\cdots <{i}_{s}\le m}(1-{\prod }_{j=1}^{s}(1-\frac{F{b}_{{i}_{j}}}{2v}))\right)}^{\frac{1}{{C}_{m}^{s}}}\right)}^\frac{1}{s}}\).
Thus,\(LNMS{M}^{(s)}({A}_{1},{A}_{2},...,{A}_{m})\le LNMS{M}^{(s)}({B}_{1},{B}_{2},...,{B}_{m})\).
Appendix B
Proof 4
(1) Based on Definition 3, \({\sum }_{j=1}^{s}{A}_{{i}_{j}}=({l}_{2v-2v{\prod }_{j=1}^{s}(1-\frac{{T}_{{i}_{j}}}{2v})},{l}_{2v{\prod }_{j=1}^{s}\frac{{I}_{{i}_{j}}}{2v}},{l}_{2v{\prod }_{j=1}^{s}\frac{{F}_{{i}_{j}}}{2v}})\)
then \(LNMS{M}^{(s)}({A}_{1},{A}_{2},...,{A}_{m})\)
(2) As \(0\le {T}_{{i}_{j}}\le 2v\Rightarrow 0\le 1-\frac{{T}_{{i}_{j}}}{2v}\le 1\Rightarrow 0\le 1-{\prod }_{j=1}^{s}(1-\frac{{T}_{{i}_{j}}}{2v})\le 1\)
then \(0\le 2v-2v{\left(1-{{\prod }_{1\le {i}_{1}<\cdots <{i}_{s}\le m}\left(1-{\prod }_{j=1}^{s}(1-\frac{{T}_{{i}_{j}}}{2v})\right)}^{\frac{1}{{C}_{m}^{s}}}\right)}^\frac{1}{s}\le 2v\); and \(0\le {I}_{{i}_{j}}\le 2v\) \(\Rightarrow\) \(0\le \frac{{I}_{{i}_{j}}}{2v}\le 1\) \(\Rightarrow\) \(0\le 1-{\prod }_{j=1}^{s}\frac{{I}_{{i}_{j}}}{2v}\le 1\) \(\Rightarrow 0\le {{\prod }_{1\le {i}_{1}<\cdots <{i}_{s}\le m}\left(1-{\prod }_{j=1}^{s}\frac{{I}_{{i}_{j}}}{2v}\right)}^{\frac{1}{{C}_{m}^{s}}}\le 1\) \(\Rightarrow\) \(0\le {\left(1-{{\prod }_{1\le {i}_{1}<\cdots <{i}_{s}\le m}\left(1-{\prod }_{j=1}^{s}\frac{{I}_{{i}_{j}}}{2v}\right)}^{\frac{1}{{C}_{m}^{s}}}\right)}^\frac{1}{s}\le 1\), then \(0\le 2v{\left(1-{{\prod }_{1\le {i}_{1}<\cdots <{i}_{s}\le m}\left(1-{\prod }_{j=1}^{s}\frac{{I}_{{i}_{j}}}{2v}\right)}^{\frac{1}{{C}_{m}^{s}}}\right)}^\frac{1}{s}\le 2v\).
Similarly, \(0\le 2v{\left(1-{{\prod }_{1\le {i}_{1}<\cdots <{i}_{s}\le m}\left(1-{\prod }_{j=1}^{s}\frac{{F}_{{i}_{j}}}{2v}\right)}^{\frac{1}{{C}_{m}^{s}}}\right)}^\frac{1}{s}\le 2v\). Thus, \(LNDMS{M}^{(s)}({A}_{1},{A}_{2},...,{A}_{m})\) is still a LNN.
Combined (1) and (2), Proof 3 now is completed.
Rights and permissions
About this article
Cite this article
Luo, Sz., Xing, Ln. & Ren, T. Performance Evaluation of Human Resources Based on Linguistic Neutrosophic Maclaurin Symmetric Mean Operators. Cogn Comput 14, 547–562 (2022). https://doi.org/10.1007/s12559-021-09963-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12559-021-09963-1