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 existing num...
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th-cmuir.6653943832-609762018-09-10T04:06:46Z Dynamic adjustment of age distribution in human resource management by genetic algorithms Napat Harnpornchai Nopasit Chakpitak Tirapot Chandarasupsang Tuang Ath Chaikijkosi Keshav Dahal Computer Science Mathematics 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. © 2007 IEEE. 2018-09-10T04:02:21Z 2018-09-10T04:02:21Z 2007-12-01 Conference Proceeding 2-s2.0-79955301302 10.1109/CEC.2007.4424611 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=79955301302&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/60976 |
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Computer Science Mathematics Napat Harnpornchai Nopasit Chakpitak Tirapot Chandarasupsang Tuang Ath Chaikijkosi Keshav Dahal 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. © 2007 IEEE. |
format |
Conference Proceeding |
author |
Napat Harnpornchai Nopasit Chakpitak Tirapot Chandarasupsang Tuang Ath Chaikijkosi Keshav Dahal |
author_facet |
Napat Harnpornchai Nopasit Chakpitak Tirapot Chandarasupsang Tuang Ath Chaikijkosi Keshav Dahal |
author_sort |
Napat Harnpornchai |
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 |
2018 |
url |
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=79955301302&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/60976 |
_version_ |
1681425535379439616 |