Incorporating Multiskilling and Learning in the Optimization of Crew Composition

Ahmadian Fard Fini, A; Rashidi, TH; Akbarnezhad, A; Travis Waller, S

Incorporating Multiskilling and Learning in the Optimization of Crew Composition

Ahmadian Fard Fini, A

Rashidi, TH Akbarnezhad, A Travis Waller, S

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Publication Type:: Journal Article
Citation:: Journal of Construction Engineering and Management, 2016, 142 (5)
Issue Date:: 2016-05-01

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Field	Value	Language
dc.contributor.author	Ahmadian Fard Fini, A https://orcid.org/0000-0001-7681-1270	en_US
dc.contributor.author	Rashidi, TH	en_US
dc.contributor.author	Akbarnezhad, A	en_US
dc.contributor.author	Travis Waller, S	en_US
dc.date.issued	2016-05-01	en_US
dc.identifier.citation	Journal of Construction Engineering and Management, 2016, 142 (5)	en_US
dc.identifier.issn	0733-9364	en_US
dc.identifier.uri	http://hdl.handle.net/10453/118490
dc.description.abstract	© 2015 American Society of Civil Engineers. The presence of multiskilled workers in a crew can increase the crew's productivity through reducing inefficiencies and supervision requirements, while also providing on-the-job learning opportunities for single-skilled workers. The effect of the presence of multiskilled workers on the learning rate of workers, which is also a function of skill level and experience, and thus on the crew's productivity, is especially significant in repetitive construction projects. This paper presents a mathematical model for identifying the optimal combination of single-skilled and multiskilled workers with different levels of experience in the crew to minimize the duration of construction projects by accounting for the overlapping effects of multiskilling, skill level, and learning on the crew's productivity. The model is applied to an illustrative case project to demonstrate the practicality of the model. The optimum crew compositions for different activities involved in the case project are identified using a solution technique which combines constraint programming (CP), statistical analysis (SA), and a genetic algorithm (GA).	en_US
dc.relation.ispartof	Journal of Construction Engineering and Management	en_US
dc.relation.isbasedon	10.1061/(ASCE)CO.1943-7862.0001085	en_US
dc.subject.classification	Building & Construction	en_US
dc.title	Incorporating Multiskilling and Learning in the Optimization of Crew Composition	en_US
dc.type	Journal Article
utslib.citation.volume	5	en_US
utslib.citation.volume	142	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	closed_access
pubs.issue	5	en_US
pubs.publication-status	Published	en_US
pubs.volume	142	en_US

Abstract:

© 2015 American Society of Civil Engineers. The presence of multiskilled workers in a crew can increase the crew's productivity through reducing inefficiencies and supervision requirements, while also providing on-the-job learning opportunities for single-skilled workers. The effect of the presence of multiskilled workers on the learning rate of workers, which is also a function of skill level and experience, and thus on the crew's productivity, is especially significant in repetitive construction projects. This paper presents a mathematical model for identifying the optimal combination of single-skilled and multiskilled workers with different levels of experience in the crew to minimize the duration of construction projects by accounting for the overlapping effects of multiskilling, skill level, and learning on the crew's productivity. The model is applied to an illustrative case project to demonstrate the practicality of the model. The optimum crew compositions for different activities involved in the case project are identified using a solution technique which combines constraint programming (CP), statistical analysis (SA), and a genetic algorithm (GA).

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http://hdl.handle.net/10453/118490