• DocumentCode
    2690151
  • Title

    Dynamic adjustment of age distribution in Human Resource Management by genetic algorithms

  • Author

    Harnpornchai, N. ; Chakpitak, Nopasit ; Chandarasupsang, Tirapot ; Chaikijkosi, Tuang-Ath ; Dahal, Keshav

  • Author_Institution
    Chiang Mai Univ., Chiang Mai
  • fYear
    2007
  • fDate
    25-28 Sept. 2007
  • Firstpage
    1234
  • Lastpage
    1239
  • Abstract
    Adjustment of a given age distribution to a desired age distribution within a required time frame is dynamically performed for the purpose of human resource (HR) planning in human resource management (HRM). The adjustment process is carried out by adding the adjustment magnitudes to the existing number of employees at the selected age groups on the yearly basis. A model of a discrete dynamical system is employed to emulate the evolution of the age distribution used under the adjustment process. Genetic algorithms (GA) is applied for determining the adjustment magnitudes that influence the dynamics of the system. An interesting aspect of the problem lies in the high number of constraints; though the constraints are fundamental, they are considerably higher in number than in many other optimization problems. An adaptive penalty scheme is proposed for handling the constraints. Numerical examples show that GA with the utilized adaptive penalty scheme provides potential means for HR planning in HRM.
  • Keywords
    genetic algorithms; human resource management; age distribution; discrete dynamical system; dynamic adjustment; genetic algorithms; human resource management; human resource planning; Art; Constraint optimization; Educational institutions; Genetic algorithms; Human resource management; Informatics; Knowledge management; Large-scale systems; Remuneration; Technology planning; Genetic Algorithms (GA); Human Resource (HR) planning; Human Resource Management (HRM); age distribution; dynamic systems; multiple constraints;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Evolutionary Computation, 2007. CEC 2007. IEEE Congress on
  • Conference_Location
    Singapore
  • Print_ISBN
    978-1-4244-1339-3
  • Electronic_ISBN
    978-1-4244-1340-9
  • Type

    conf

  • DOI
    10.1109/CEC.2007.4424611
  • Filename
    4424611