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Research of a Multi-Level Organization Human Resource Network Optimization Model and an Improved Late Acceptance Hill Climbing Algorithm

Author

Listed:
  • Jingbo Huang

    (College of System Engineering, National University of Defense Technology, Changsha 410073, China
    These authors contributed equally to this work.)

  • Jiting Li

    (Academy of Military Sciences, Beijing 100071, China
    These authors contributed equally to this work.)

  • Yonghao Du

    (College of System Engineering, National University of Defense Technology, Changsha 410073, China)

  • Yanjie Song

    (College of System Engineering, National University of Defense Technology, Changsha 410073, China)

  • Jian Wu

    (College of System Engineering, National University of Defense Technology, Changsha 410073, China)

  • Feng Yao

    (College of System Engineering, National University of Defense Technology, Changsha 410073, China)

  • Pei Wang

    (College of System Engineering, National University of Defense Technology, Changsha 410073, China)

Abstract

Complex hierarchical structures and diverse personnel mobility pose challenges for many multi-level organizations. The difficulty of reasonable human resource planning in multi-level organizations is mainly caused by ignoring the hierarchical structure. To address the above problems, firstly, a multi-level organization human resource network optimization model is constructed by representing the turnover situation of multi-level organizations in a dimensional manner as a multi-level network. Secondly, we propose an improved late acceptance hill climbing based on tabu and retrieval strategy (TR-LAHC) and designed two intelligent optimization operators. Finally, the TR-LAHC algorithm is compared with other classical algorithms to prove that the algorithm provides the best solution and can effectively solve the personnel mobility planning problem in multi-level organizations.

Suggested Citation

  • Jingbo Huang & Jiting Li & Yonghao Du & Yanjie Song & Jian Wu & Feng Yao & Pei Wang, 2023. "Research of a Multi-Level Organization Human Resource Network Optimization Model and an Improved Late Acceptance Hill Climbing Algorithm," Mathematics, MDPI, vol. 11(23), pages 1-19, November.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:23:p:4813-:d:1289985
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    References listed on IDEAS

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    1. Nathaniel D. Bastian & Pat McMurry & Lawrence V. Fulton & Paul M. Griffin & Shisheng Cui & Thor Hanson & Sharan Srinivas, 2015. "The AMEDD Uses Goal Programming to Optimize Workforce Planning Decisions," Interfaces, INFORMS, vol. 45(4), pages 305-324, August.
    2. Komarudin & Tim De Feyter & Marie-Anne Guerry & Greet Vanden Berghe, 2016. "Balancing desirability and promotion steadiness in partially stochastic manpower planning systems," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 45(6), pages 1805-1818, March.
    3. Burke, Edmund K. & Bykov, Yuri, 2017. "The late acceptance Hill-Climbing heuristic," European Journal of Operational Research, Elsevier, vol. 258(1), pages 70-78.
    4. Mark Zais & Dan Zhang, 2016. "A Markov chain model of military personnel dynamics," International Journal of Production Research, Taylor & Francis Journals, vol. 54(6), pages 1863-1885, March.
    5. X Zhu & H D Sherali, 2009. "Two-stage workforce planning under demand fluctuations and uncertainty," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(1), pages 94-103, January.
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