Using genetic algorithms to improve crew allocation process in labour-intensive industries

Hdl Handle:
http://hdl.handle.net/10149/95222
Title:
Using genetic algorithms to improve crew allocation process in labour-intensive industries
Book Title:
Proceedings of the 2009 ASCE international workshop on computing in civil engineering
Authors:
Dawood, N. N. (Nashwan); Al-Bazi, A. F. J. (Ammar)
Editors:
Caldas, C. H. (Carlos); O'Brien, W. J. (William)
Affiliation:
University of Teesside. School of Science and Technology. Center for Construction Innovation Research.
Citation:
Dawood, N. N. and Al-Bazi, A. F. J. (2009) 'Using genetic algorithms to improve crew allocation process in labour-intensive industries', 2009 ASCE international workshop on computing in civil engineering, Austin, Texas, June 24 - 27, in Caldas, C. H. and O'Brien, W. J. (eds) Proceedings of the 2009 ASCE international workshop on computing in civil engineering. Virginia: American Society of Civil Engineers, pp.166-175.
Publisher:
American Society of Civil Engineers
Conference:
2009 ASCE international workshop on computing in civil engineering, Austin, Texas, June 24 - 27, 2009
Issue Date:
2009
URI:
http://hdl.handle.net/10149/95222
DOI:
10.1061/41052(346)17
Abstract:
The high cost of skilled workers in labour-intensive production industries has motivated senior production managers to identify the best allocation strategy of crews of workers to appropriate processes. The aim of this paper is to develop a crew allocation system using Genetic Algorithms-based simulation modeling. The objective is to optimally allocate crews of workers to labour-intensive production industries to minimise labour costs. In this paper, a simulation-based Genetic Algorithm (GA) system dubbed "SIM-Crew" is developed to simulate the physical processes of a labour-driven facility. The GA is tailored to be embedded with the developed simulation model for improved solution searching. A chromosome structure is designed to apply such problems and a probabilistic selection of promising chromosomes is applied as a selection strategy, n-points crossover and mutation strategies are designed to add more randomness to the searching process. A case study in the precast industry is presented to demonstrate and validate the model.
Type:
Meetings and Proceedings; Book Chapter
Language:
en
Keywords:
genetic algorithms; labour-intensive; production industries; allocation strategy; workers
Series/Report no.:
Volume 346
ISBN:
9780784410523
Rights:
Subject to 90 day embargo, author can archive post-print (ie final draft post-refereeing). For full details see http://www.sherpa.ac.uk/romeo/ [Accessed 30/03/2010]
Citation Count:
0 [Scopus, 30/03/2010]

Full metadata record

DC FieldValue Language
dc.contributor.authorDawood, N. N. (Nashwan)en
dc.contributor.authorAl-Bazi, A. F. J. (Ammar)en
dc.contributor.editorCaldas, C. H. (Carlos)en
dc.contributor.editorO'Brien, W. J. (William)en
dc.date.accessioned2010-03-30T09:21:51Z-
dc.date.available2010-03-30T09:21:51Z-
dc.date.issued2009-
dc.identifier.isbn9780784410523-
dc.identifier.doi10.1061/41052(346)17-
dc.identifier.urihttp://hdl.handle.net/10149/95222-
dc.description.abstractThe high cost of skilled workers in labour-intensive production industries has motivated senior production managers to identify the best allocation strategy of crews of workers to appropriate processes. The aim of this paper is to develop a crew allocation system using Genetic Algorithms-based simulation modeling. The objective is to optimally allocate crews of workers to labour-intensive production industries to minimise labour costs. In this paper, a simulation-based Genetic Algorithm (GA) system dubbed "SIM-Crew" is developed to simulate the physical processes of a labour-driven facility. The GA is tailored to be embedded with the developed simulation model for improved solution searching. A chromosome structure is designed to apply such problems and a probabilistic selection of promising chromosomes is applied as a selection strategy, n-points crossover and mutation strategies are designed to add more randomness to the searching process. A case study in the precast industry is presented to demonstrate and validate the model.en
dc.language.isoenen
dc.publisherAmerican Society of Civil Engineersen
dc.relation.ispartofseriesVolume 346-
dc.rightsSubject to 90 day embargo, author can archive post-print (ie final draft post-refereeing). For full details see http://www.sherpa.ac.uk/romeo/ [Accessed 30/03/2010]en
dc.subjectgenetic algorithmsen
dc.subjectlabour-intensiveen
dc.subjectproduction industriesen
dc.subjectallocation strategyen
dc.subjectworkersen
dc.titleUsing genetic algorithms to improve crew allocation process in labour-intensive industriesen
dc.typeMeetings and Proceedingsen
dc.typeBook Chapteren
dc.contributor.departmentUniversity of Teesside. School of Science and Technology. Center for Construction Innovation Research.en
dc.title.bookProceedings of the 2009 ASCE international workshop on computing in civil engineeringen
dc.identifier.conference2009 ASCE international workshop on computing in civil engineering, Austin, Texas, June 24 - 27, 2009en
ref.citationcount0 [Scopus, 30/03/2010]en
or.citation.harvardDawood, N. N. and Al-Bazi, A. F. J. (2009) 'Using genetic algorithms to improve crew allocation process in labour-intensive industries', 2009 ASCE international workshop on computing in civil engineering, Austin, Texas, June 24 - 27, in Caldas, C. H. and O'Brien, W. J. (eds) Proceedings of the 2009 ASCE international workshop on computing in civil engineering. Virginia: American Society of Civil Engineers, pp.166-175.-
prism.startingPage166-
prism.endingPage175-
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