Dynamic Modeling of Workforce Planning for Infrastructure Projects
© 2016 American Society of Civil Engineers.Workforce planning (WP) for infrastructure projects provides a readily available pool of skilled labor that can deliver a nation's societal needs. However, achieving a robust and reliable workforce prediction is a constant challenge, as a variety of va...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Published: |
ASCE
2016
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| Online Access: | http://hdl.handle.net/20.500.11937/58667 |
| _version_ | 1848760317167796224 |
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| author | Sing, M. Love, Peter Edwards, D. Liu, Henry |
| author_facet | Sing, M. Love, Peter Edwards, D. Liu, Henry |
| author_sort | Sing, M. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 2016 American Society of Civil Engineers.Workforce planning (WP) for infrastructure projects provides a readily available pool of skilled labor that can deliver a nation's societal needs. However, achieving a robust and reliable workforce prediction is a constant challenge, as a variety of variables and factors must be considered. Despite various forecasting techniques and approaches being developed, government's worldwide continue to produce inaccurate forecasts and consequently fail to maintain the balanced workforce required to deliver infrastructure projects. To address this problem, a system dynamics (SD) model for the construction and civil engineering industry is developed, as traditional WP modeling approaches are static and unable to accommodate the changing complex dynamics that influence workforce supply and demand. The SD model is tested and used to formulate training policies that ensure workforce equilibrium and in turn, nurture sustainable infrastructure development. |
| first_indexed | 2025-11-14T10:13:51Z |
| format | Journal Article |
| id | curtin-20.500.11937-58667 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:13:51Z |
| publishDate | 2016 |
| publisher | ASCE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-586672017-11-24T05:47:22Z Dynamic Modeling of Workforce Planning for Infrastructure Projects Sing, M. Love, Peter Edwards, D. Liu, Henry © 2016 American Society of Civil Engineers.Workforce planning (WP) for infrastructure projects provides a readily available pool of skilled labor that can deliver a nation's societal needs. However, achieving a robust and reliable workforce prediction is a constant challenge, as a variety of variables and factors must be considered. Despite various forecasting techniques and approaches being developed, government's worldwide continue to produce inaccurate forecasts and consequently fail to maintain the balanced workforce required to deliver infrastructure projects. To address this problem, a system dynamics (SD) model for the construction and civil engineering industry is developed, as traditional WP modeling approaches are static and unable to accommodate the changing complex dynamics that influence workforce supply and demand. The SD model is tested and used to formulate training policies that ensure workforce equilibrium and in turn, nurture sustainable infrastructure development. 2016 Journal Article http://hdl.handle.net/20.500.11937/58667 10.1061/(ASCE)ME.1943-5479.0000463 ASCE restricted |
| spellingShingle | Sing, M. Love, Peter Edwards, D. Liu, Henry Dynamic Modeling of Workforce Planning for Infrastructure Projects |
| title | Dynamic Modeling of Workforce Planning for Infrastructure Projects |
| title_full | Dynamic Modeling of Workforce Planning for Infrastructure Projects |
| title_fullStr | Dynamic Modeling of Workforce Planning for Infrastructure Projects |
| title_full_unstemmed | Dynamic Modeling of Workforce Planning for Infrastructure Projects |
| title_short | Dynamic Modeling of Workforce Planning for Infrastructure Projects |
| title_sort | dynamic modeling of workforce planning for infrastructure projects |
| url | http://hdl.handle.net/20.500.11937/58667 |