Dynamic adjustment of age distribution in Human Resource Management by genetic algorithms.
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 existin...
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ndltd-BRADFORD-oai-bradscholars.brad.ac.uk-10454-24722019-08-31T03:02:27Z Dynamic adjustment of age distribution in Human Resource Management by genetic algorithms. Harnpornchai, N. Chakpitak, N. Chandarasupsang, T. Tuang-Ath Chaikijkosi, T. Dahal, Keshav P. Human Resource Management (HRM) Human Resource (HR) planning Genetic Algorithms (GA) Multiple constraints Dynamic systems Age distribution 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. 2009-03-12T15:26:04Z 2009-03-12T15:26:04Z 2007 Conference paper published version paper Harnpornchai, N.; Chakpitak, N.; Chandarasupsang, T.; Tuang-Ath Chaikijkosi; Dahal, K. (2007) Dynamic adjustment of age distribution in Human Resource Management by genetic algorithms. In: IEEE Congress on Evolutionary Computation (CEC 2007), 25-28 Sept. 2007, Singapore, pp.1234 - 1239. http://hdl.handle.net/10454/2472 en http://ieeexplore.ieee.org/search/wrapper.jsp?arnumber=4424611 Copyright © [2007] IEEE. Reprinted from IEEE Congress on Evolutionary Computation, 2007 (CEC 2007). This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Bradford's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubspermissions@ ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it. |
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language |
en |
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topic |
Human Resource Management (HRM) Human Resource (HR) planning Genetic Algorithms (GA) Multiple constraints Dynamic systems Age distribution |
spellingShingle |
Human Resource Management (HRM) Human Resource (HR) planning Genetic Algorithms (GA) Multiple constraints Dynamic systems Age distribution Harnpornchai, N. Chakpitak, N. Chandarasupsang, T. Tuang-Ath Chaikijkosi, T. Dahal, Keshav P. Dynamic adjustment of age distribution in Human Resource Management by genetic algorithms. |
description |
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. |
author |
Harnpornchai, N. Chakpitak, N. Chandarasupsang, T. Tuang-Ath Chaikijkosi, T. Dahal, Keshav P. |
author_facet |
Harnpornchai, N. Chakpitak, N. Chandarasupsang, T. Tuang-Ath Chaikijkosi, T. Dahal, Keshav P. |
author_sort |
Harnpornchai, N. |
title |
Dynamic adjustment of age distribution in Human Resource Management by genetic algorithms. |
title_short |
Dynamic adjustment of age distribution in Human Resource Management by genetic algorithms. |
title_full |
Dynamic adjustment of age distribution in Human Resource Management by genetic algorithms. |
title_fullStr |
Dynamic adjustment of age distribution in Human Resource Management by genetic algorithms. |
title_full_unstemmed |
Dynamic adjustment of age distribution in Human Resource Management by genetic algorithms. |
title_sort |
dynamic adjustment of age distribution in human resource management by genetic algorithms. |
publishDate |
2009 |
url |
http://hdl.handle.net/10454/2472 |
work_keys_str_mv |
AT harnpornchain dynamicadjustmentofagedistributioninhumanresourcemanagementbygeneticalgorithms AT chakpitakn dynamicadjustmentofagedistributioninhumanresourcemanagementbygeneticalgorithms AT chandarasupsangt dynamicadjustmentofagedistributioninhumanresourcemanagementbygeneticalgorithms AT tuangathchaikijkosit dynamicadjustmentofagedistributioninhumanresourcemanagementbygeneticalgorithms AT dahalkeshavp dynamicadjustmentofagedistributioninhumanresourcemanagementbygeneticalgorithms |
_version_ |
1719239365431918592 |