Optimization of job allocation in construction organizations to maximize workers' career development opportunities

Shahbazi, B; Akbarnezhad, A; Rey, D; Ahmadian Fard Fini, A; Loosemore, M

Optimization of job allocation in construction organizations to maximize workers' career development opportunities

Shahbazi, B Akbarnezhad, A Rey, D Ahmadian Fard Fini, A

Loosemore, M

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Publication Type:: Journal Article
Citation:: Journal of Construction Engineering and Management, 2019, 145 (6)
Issue Date:: 2019-06-01

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Field	Value	Language
dc.contributor.author	Shahbazi, B	en_US
dc.contributor.author	Akbarnezhad, A	en_US
dc.contributor.author	Rey, D	en_US
dc.contributor.author	Ahmadian Fard Fini, A https://orcid.org/0000-0001-7681-1270	en_US
dc.contributor.author	Loosemore, M https://orcid.org/0000-0002-3189-4655	en_US
dc.date.issued	2019-06-01	en_US
dc.identifier.citation	Journal of Construction Engineering and Management, 2019, 145 (6)	en_US
dc.identifier.issn	0733-9364	en_US
dc.identifier.uri	http://hdl.handle.net/10453/133410
dc.description.abstract	© 2019 American Society of Civil Engineers. Workforce planning in the construction industry too often ignores the symbiotic relationship between employee and employer objectives by overly concentrating on corporate objectives such as maximizing productivity at the expense of construction workers' career development needs. Overall, the consequence of this approach is suboptimal performance. To address this problem, this paper presents an innovative multiobjective model that enables managers to optimize the relationship between these interdependent corporate priorities. The proposed model was implemented and solved using mixed-integer nonlinear programming on a case study involving the allocation of tasks to employees with different skill levels in a multidisciplinary engineering consulting company. While leading to a small loss of productivity, the results show a significant improvement in the career development of workers compared to conventional productivity-oriented workforce planning models, with on average 8.6% improvement in employees' closeness to their ideal skill set. Furthermore, the model produced Pareto-optimal points and a Pareto curve that enabled client-model users to select optimum job allocation based on their preferences. This research represents a paradigm shift toward a new class of socially responsible workforce planning models in which the objectives of both employees and employers are optimized.	en_US
dc.relation.ispartof	Journal of Construction Engineering and Management	en_US
dc.relation.isbasedon	10.1061/(ASCE)CO.1943-7862.0001652	en_US
dc.subject.classification	Building & Construction	en_US
dc.title	Optimization of job allocation in construction organizations to maximize workers' career development opportunities	en_US
dc.type	Journal Article
utslib.citation.volume	6	en_US
utslib.citation.volume	145	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	1202 Building	en_US
utslib.for	0905 Civil Engineering	en_US
utslib.for	1504 Commercial Services	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Design, Architecture and Building
pubs.organisational-group	/University of Technology Sydney/Faculty of Design, Architecture and Building/School of Built Environment
utslib.copyright.status	open_access
pubs.issue	6	en_US
pubs.publication-status	Published	en_US
pubs.volume	145	en_US

Abstract:

© 2019 American Society of Civil Engineers. Workforce planning in the construction industry too often ignores the symbiotic relationship between employee and employer objectives by overly concentrating on corporate objectives such as maximizing productivity at the expense of construction workers' career development needs. Overall, the consequence of this approach is suboptimal performance. To address this problem, this paper presents an innovative multiobjective model that enables managers to optimize the relationship between these interdependent corporate priorities. The proposed model was implemented and solved using mixed-integer nonlinear programming on a case study involving the allocation of tasks to employees with different skill levels in a multidisciplinary engineering consulting company. While leading to a small loss of productivity, the results show a significant improvement in the career development of workers compared to conventional productivity-oriented workforce planning models, with on average 8.6% improvement in employees' closeness to their ideal skill set. Furthermore, the model produced Pareto-optimal points and a Pareto curve that enabled client-model users to select optimum job allocation based on their preferences. This research represents a paradigm shift toward a new class of socially responsible workforce planning models in which the objectives of both employees and employers are optimized.

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http://hdl.handle.net/10453/133410