IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i23p4813-d1289985.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/23/4813/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/23/4813/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Oussama Mazari-Abdessameud & Filip Van Utterbeeck & Guy Van Acker & Marie-Anne Guerry, 2020. "Multidimensional military manpower planning based on a career path approach," Operations Management Research, Springer, vol. 13(3), pages 249-263, December.
    2. Turan, Hasan Hüseyin & Jalalvand, Fatemeh & Elsawah, Sondoss & Ryan, Michael J., 2022. "A joint problem of strategic workforce planning and fleet renewal: With an application in defense," European Journal of Operational Research, Elsevier, vol. 296(2), pages 615-634.
    3. Fagui Liu & Lvshengbiao Wang & Mengke Gui & Yang Zhang & Yulin Lan & Chengqi Lai & Boyuan Zhu, 2023. "A hybrid heuristic algorithm for urban distribution with simultaneous pickup-delivery and time window," Journal of Heuristics, Springer, vol. 29(2), pages 269-311, June.
    4. Bastian, Nathaniel D. & Lunday, Brian J. & Fisher, Christopher B. & Hall, Andrew O., 2020. "Models and methods for workforce planning under uncertainty: Optimizing U.S. Army cyber branch readiness and manning," Omega, Elsevier, vol. 92(C).
    5. Corominas, Albert & Lusa, Amaia & Olivella, Jordi, 2012. "A detailed workforce planning model including non-linear dependence of capacity on the size of the staff and cash management," European Journal of Operational Research, Elsevier, vol. 216(2), pages 445-458.
    6. G M Campbell, 2011. "A two-stage stochastic program for scheduling and allocating cross-trained workers," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(6), pages 1038-1047, June.
    7. Georgiou, Andreas C. & Thanassoulis, Emmanuel & Papadopoulou, Alexandra, 2022. "Using data envelopment analysis in markovian decision making," European Journal of Operational Research, Elsevier, vol. 298(1), pages 276-292.
    8. Restrepo, María I. & Gendron, Bernard & Rousseau, Louis-Martin, 2017. "A two-stage stochastic programming approach for multi-activity tour scheduling," European Journal of Operational Research, Elsevier, vol. 262(2), pages 620-635.
    9. Ceschia, Sara & Di Gaspero, Luca & Schaerf, Andrea, 2023. "Educational timetabling: Problems, benchmarks, and state-of-the-art results," European Journal of Operational Research, Elsevier, vol. 308(1), pages 1-18.
    10. Jayanth Krishna Mogali & Joris Kinable & Stephen F. Smith & Zachary B. Rubinstein, 2021. "Scheduling for multi-robot routing with blocking and enabling constraints," Journal of Scheduling, Springer, vol. 24(3), pages 291-318, June.
    11. Valeva, Silviya & Hewitt, Mike & Thomas, Barrett W. & Brown, Kenneth G., 2017. "Balancing flexibility and inventory in workforce planning with learning," International Journal of Production Economics, Elsevier, vol. 183(PA), pages 194-207.
    12. Tohidi, Mohammad & Kazemi Zanjani, Masoumeh & Contreras, Ivan, 2021. "A physician planning framework for polyclinics under uncertainty," Omega, Elsevier, vol. 101(C).
    13. Rempel, M. & Cai, J., 2021. "A review of approximate dynamic programming applications within military operations research," Operations Research Perspectives, Elsevier, vol. 8(C).
    14. Black, Ben & Ainslie, Russell & Dokka, Trivikram & Kirkbride, Christopher, 2023. "Distributionally robust resource planning under binomial demand intakes," European Journal of Operational Research, Elsevier, vol. 306(1), pages 227-242.
    15. Günay Uzun & Metin Dağdeviren & Mehmet Kabak, 2016. "Determining the Distribution of Coast Guard Vessels," Interfaces, INFORMS, vol. 46(4), pages 297-314, August.
    16. Tim De Feyter & Marie-Anne Guerry & Komarudin, 2017. "Optimizing cost-effectiveness in a stochastic Markov manpower planning system under control by recruitment," Annals of Operations Research, Springer, vol. 253(1), pages 117-131, June.
    17. Babak Saleck Pay & Yongjia Song, 2020. "Partition-based decomposition algorithms for two-stage Stochastic integer programs with continuous recourse," Annals of Operations Research, Springer, vol. 284(2), pages 583-604, January.
    18. Sara Ceschia & Luca Di Gaspero & Antonella Meneghetti, 2020. "Extending and Solving the Refrigerated Routing Problem," Energies, MDPI, vol. 13(23), pages 1-24, November.
    19. Correcher, Juan Francisco & Van den Bossche, Thomas & Alvarez-Valdes, Ramon & Vanden Berghe, Greet, 2019. "The berth allocation problem in terminals with irregular layouts," European Journal of Operational Research, Elsevier, vol. 272(3), pages 1096-1108.
    20. Yangming Zhou & Jin-Kao Hao & Zhen Li, 2024. "Heuristic Search for Rank Aggregation with Application to Label Ranking," INFORMS Journal on Computing, INFORMS, vol. 36(2), pages 308-326, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2023:i:23:p:4813-:d:1289985. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.