IDEAS home Printed from https://ideas.repec.org/a/taf/lstaxx/v45y2016i6p1805-1818.html
   My bibliography  Save this article

Balancing desirability and promotion steadiness in partially stochastic manpower planning systems

Author

Listed:
  • Komarudin
  • Tim De Feyter
  • Marie-Anne Guerry
  • Greet Vanden Berghe

Abstract

Current manpower planning approaches focus on satisfying future personnel needs. In the mean time, there is a lack of contribution on managing the personnel work satisfaction on the long-term. In general, long-term work satisfaction can be achieved by maintaining the preferred promotion strategy. This paper aims at balancing the strategy to fulfill the future personnel requirements while at the same time maintaining the preferred promotion strategy. Moreover, stochastic wastage is considered to better capture the real world condition of voluntarily wastage. Illustration of the model shows that it enables obtaining favorable recruitment and promotion strategies.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:lstaxx:v:45:y:2016:i:6:p:1805-1818
    DOI: 10.1080/03610926.2014.1001495
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03610926.2014.1001495
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/03610926.2014.1001495?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.

    More about this item

    Statistics

    Access and download statistics

    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:taf:lstaxx:v:45:y:2016:i:6:p:1805-1818. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/lsta .

    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.