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The talent planning model and empirical research to the key disciplines in science and technology

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Abstract

With to the impact of economic globalization, the talent construction of key disciplines in science and technology should be administrated with humanism. An analysis of existing articles shows that the research of talent development mainly relates to the following aspects: cultivating objectives, cultivator, cultivation way, and evaluation criteria. In recent years, with the continuous improvement of education system in China and the increased awareness of talents, the talent construction of key discipline in science and technology has been greatly improved. With the actuality and circumstance analysis of the talent construction of key disciplines, a talent planning model is proposed to the key disciplines in science and technology. The proposed model is III-level tree structure, of which there are 2 I-level indexes, 8 II-level indexes and 23 III-level indexes. The Analytic Hierarchy Process is employed to determine the weights of talent planning indexes. This research will make the more scientific, systematic, strategic talent planning, and adapt to the development needs of key disciplines.

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References

  1. Development and Learning in Organizations Be Structured in Managing Talent. 21(3), 31 (2007)

  2. Cunningham, I.: Talent management: Making it real. Dev. Learn. Organ. 21(2), 4–5 (2007)

    Article  Google Scholar 

  3. D’Amato, A., Herzfeldt, R.: Learning orientation, organizational commitment and talent retention across generations. J. Manag. Psychol. 23(8), 930 (2008)

    Google Scholar 

  4. Xing, L.N., Philipp, R., Chen, Y.W., Yao, X.: An evolutionary approach to the multi-depot capacitated arc routing problem. IEEE Trans. Evol. Comput. 14(3), 356–374 (2010)

    Article  Google Scholar 

  5. Xing, L.N., Chen, Y.W., Yang, K.W.: A hybrid approach combining an improved genetic algorithm and optimization strategies for the asymmetric travelling salesman problem. Eng. Appl. Artif. Intell. 21, 1370–1380 (2008)

    Article  Google Scholar 

  6. DAnnunzio–Green, N.: Managing the talent management pipeline: towards a greater understanding of senior managers’ perspectives in the hospitality and tourism sector. Int. J. Contemp. Hosp. Manag. 20(7), 807–809 (2008)

    Article  Google Scholar 

  7. Horvathova, P.: Enterprise: performance and business processes. Perspectives of Innovations. Econ. Bus. 3, 77 (2009)

    Google Scholar 

  8. Hughes, J., Rog, E.: Talent management: a strategy for improving employee recruitment, retention and engagement within hospitality organizations. Int. J. Contemp. Hosp. Manag. 20(7), 746 (2008)

    Google Scholar 

  9. Sharma, R., Bhatnagar, J.: Talent management —competency development: key to global leadership. Ind. Commer. Train. 41(3), 120 (2009)

    Article  Google Scholar 

  10. Wu, G.H., Mallipeddi, R., Suganthan, P.N., et al.: Differential evolution with multi-population based ensemble of mutation strategies. Inf. Sci. 329, 329–345 (2016)

    Article  Google Scholar 

  11. Wu, G.H.: Across neighborhood search for numerical optimization. Inf. Sci. 329, 597–618 (2016)

    Article  Google Scholar 

  12. Gong, D.W., Sun, J., Miao, Z.: A set-based genetic algorithm for interval many-objective optimization problems. IEEE Transact. Evol. Comput. 19, 1477–1495 (2016)

    Google Scholar 

  13. Liu, Y.P., Gong, D.W., Sun, J., Jin, Y.C.: A many-objective evolutionary algorithm using a one-by-one selection strategy. IEEE Transact. Cybern. (2016). doi:10.1109/TCYB.2016.2638902

  14. Shih, H.S., Huang, L.C., Shyur, H.J.: Recruitment and selection processes through an effective GDSS. Comput. Math. Appl. 50(10—-12), 1543–1558 (2005)

    Article  MATH  Google Scholar 

  15. Stavrou, E.T., Kleanthous, T., Anastasiou, T.: Leadership personality and firm culture during hereditary transitions in family firms: model development and empirical investigation. J. Small Bus. Manag. 43(2), 187–206 (2005)

    Article  Google Scholar 

  16. Xing, L.N., Chen, Y.W., Shen, X.S.: A constraint satisfaction adaptive neural network with dynamic model for job-shop scheduling problem. Lect. Notes Comput. Sci. 3973, 927–932 (2006)

    Article  Google Scholar 

  17. Xing, L.N., Chen, Y.W., Cai, H.P.: An intelligent genetic algorithm designed for global opti-mization of multi-minima functions. Appl. Math. Comput. 178, 355–371 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  18. Meade, L.: Strategic analysis of logistics and supply chain management systems using the an-alytical network process. Transp. Res. E. 34(4), 201–215 (1998)

    Article  Google Scholar 

  19. Saaty, T.L.: Fundamentals of the analytic network process—dependence and feedback in decision-making with a single network. J. Syst. Sci. Syst. Eng. 13(2), 129–157 (2004)

    Article  Google Scholar 

  20. Xing, L.N., Chen, Y.W., Wang, P., et al.: A knowledge-based ant colony optimization for flexible job shop scheduling problems. Appl. Soft Comput. 10, 888–896 (2010)

    Article  Google Scholar 

  21. Zhang, Y.H., Jeon, B., Xu, D.H., et al.: Image segmentation by generalized hierarchical fuzzy C-means algorithm. J. Intell. Fuzzy Syst 28(2), 961–973 (2015)

    Google Scholar 

  22. Xie, S.D., Wang, Y.X.: Construction of tree network with limited delivery latency in homogeneous wireless sensor networks. Wirel. Pers. Commun. 78(1), 231–246 (2014)

    Article  Google Scholar 

  23. Wen, W.Z., Sha, L., Xue, Y., et al.: A rapid learning algorithm for vehicle classification. Inf. Sci. 295(1), 395–406 (2015)

    Article  Google Scholar 

  24. Wu, G.H., Liu, J., Ma, M.H., et al.: A two-phase scheduling method with the consideration of task clustering for earth observing satellites. Comput. Op. Res. 40, 1884–1894 (2013)

    Article  MATH  Google Scholar 

  25. Zhang, Y.H., Sun, X.M., Wang, B.W.: Efficient algorithm for K-barrier coverage based on integer linear programming, China. Communications 13(7), 16–23 (2016)

    Google Scholar 

  26. Wang, R., Purshouse, R.C., Fleming, P.J.: Preference-inspired co-evolutionary algorithms for many-objective optimisation. IEEE Transact. Evol. Comput. 17, 474–494 (2013)

    Article  Google Scholar 

  27. Wang, R., Ishibuchi, H., Zhou, Z., Liao, T., Zhang, T.: Localized weighted sum method for many-objective optimization. IEEE Transact. Evol. Comput. 99, 176–190 (2016)

    Google Scholar 

  28. Xing, L.N., Chen, Y.W., Shen, X.S.: Multiprogramming genetic algorithm for optimization problems with permutation property. Appl. Math. Comput. 185, 473–483 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  29. Wu, G.H., Pedrycz, W., Li, H.E., et al.: Coordinated planning of heterogeneous earth observation resources. IEEE Transact. Syst. Man Cybern. 46, 109–125 (2016)

    Article  Google Scholar 

  30. Wang, R., Zhang, Q., Zhang, T.: Decomposition based algorithms using Pareto adaptive scalarizing methods. IEEE Transact. Evol. Comput. 20, 821–837 (2016)

    Article  Google Scholar 

  31. Xing, L.N., Chen, Y.W., Yang, K.W.: Double layer ant colony optimization for multi-objective flexible job shop scheduling problems. New Gener. Comput. 26, 313–327 (2008)

    Article  Google Scholar 

  32. Gu, B., Sheng, V.S.: A robust regularization path algorithm for \(v-\)support vector classification. IEEE Transact. Neural Netw. Learning Syst. 28(5), 1241–1248 (2016)

    Article  Google Scholar 

  33. Gu, B., Sun, X.M., Sheng, V.S.: Structural minimax probability machine. IEEE Transact. Neural Netw. Learn. Syst. 28(7), 1646–1656 (2016)

    Article  MathSciNet  Google Scholar 

  34. Fu, Z.J., Sun, X.M., Liu, Q.: Achieving efficient cloud search services: multi-keyword ranked search over encrypted cloud data supporting parallel computing. IEICE Transact. Commun. 98, 190–200 (2015)

    Article  Google Scholar 

  35. Xing, L.N., Chen, Y.W., Yang, K.W.: An efficient search method for multi-objective flexible job shop scheduling problems. J. Intell. Manuf. 20, 283–293 (2009)

    Article  Google Scholar 

  36. Tajadin, M., Moali, M.: Why talent management. Tadbir J. 191, 62 (2006)

    Google Scholar 

  37. Wilcox, I.: Raising renaissance managers. Pharm. Exec. 25(6), 41 (2005)

    Google Scholar 

  38. Boudreau, J.W., Ramstad, P.: Talentship and the evolution of human resource management: from professional practices to strategic talent decision science. Hum. Reso. Plan. J. 28(2), 17–26 (2005)

    Google Scholar 

  39. Karsak, E.E., Sozer, S., Alptekin, S.E.: Product planning in quality function deployment using a combined analytic network process and goal programming approach. Comput. Ind. Eng. 44(1), 171–190 (2003)

    Article  Google Scholar 

  40. Wang, R., Purshouse, R.C., Fleming, P.J.: Preference-inspired co-evolutionary algorithms using weight vectors. Eur. J. Op. Res. 243, 423–441 (2015)

    Article  MATH  MathSciNet  Google Scholar 

  41. Wang, R., Purshouse, R.C., Giagkiozis, I., Fleming, P.J.: The iPICEA-g: a new hybrid evolutionary multi-criteria decision making approach using the brushing technique. Eur. J. Op. Res. 243, 442–445 (2015)

    Article  MATH  MathSciNet  Google Scholar 

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Acknowledgements

This work is supported by the Development Project of Jilin Province of China (No. 20170101006JC, 20160414009GH, 20160204022GX, 20170203002GX), China Postdoctoral Science Foundation (No. 2016M601379), Premier-Discipline Enhancement Scheme supported by Zhuhai Government and Premier Key-Discipline Enhancement Scheme supported Guangdong Government Funds. This work is also supported by the National Natural Science Foundation of China (71331008), Foundation for the Author of National Excellent Doctoral Dissertation of PR China (2014-92), the Outstanding Youth Fund Project of Hunan Provincial Natural Science Foundation (S2015J5050), the Fundamental Research Funds for the Central Universities (531107050772) and Shenzhen Basic Research Project for Development of Science and Technology (JCYJ20160530141956915).

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Authors and Affiliations

  1. College of Computer Science and Technology, Jilin University, Changchun, 130012, People’s Republic of China

    Hao Xu, Dongrui Wu & Lan Huang

  2. School of Mathematics and Big Data, Foshan University, Foshan, 528000, People’s Republic of China

    Lining Xing

  3. College of Engineering, Shanghai Polytechnic University, Shanghai, 201209, People’s Republic of China

    Lining Xing

  4. College of Information System and Management, National University of Defense Technology, Changsha, 410073, People’s Republic of China

    Lining Xing

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  1. Hao Xu
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  4. Lan Huang
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Correspondence to Lan Huang.

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Xu, H., Wu, D., Xing, L. et al. The talent planning model and empirical research to the key disciplines in science and technology. Cluster Comput 20, 3275–3286 (2017). https://doi.org/10.1007/s10586-017-1060-8

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  • DOI: https://doi.org/10.1007/s10586-017-1060-8

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