An intelligent crew allocation system for the precast manufacturing systems: Railway sleepers precast concrete as a case study

Hdl Handle:
http://hdl.handle.net/10149/95151
Title:
An intelligent crew allocation system for the precast manufacturing systems: Railway sleepers precast concrete as a case study
Book Title:
Proceedings, annual conference - Canadian society for civil engineering
Authors:
Al-Bazi, A. F. J. (Ammar); Dawood, N. N. (Nashwan)
Affiliation:
University of Teesside. School of Science and Technology. Center for Construction Innovation Research.
Citation:
Al-Bazi, A. F. J. and Dawood, N. N. (2009) 'An intelligent crew allocation system for the precast manufacturing systems: Railway sleepers precast concrete as a case study', Canadian society for civil engineering annual conference, St. Johns, Canada, May 27 - 30, in Proceedings, annual conference - Canadian society for civil engineering; Vol. 2. Curran Associates, Inc., pp.777-786.
Publisher:
Curran Associates, Inc.
Conference:
Canadian society for civil engineering annual conference, 2009, St. Johns, Newfoundland, Canada, May 27 - 30, 2009
Issue Date:
Oct-2009
URI:
http://hdl.handle.net/10149/95151
Additional Links:
http://www.proceedings.com/06035.html
Abstract:
This paper presents an innovative approach for best allocation of crews of workers on production processes by combining flow-chart based simulation tool with a powerful genetic optimization procedure. The proposed approach determines the least costly and most productive crew allocation on a set of production processes. IDEFO diagrams were developed to provide generic and functional diagrams for each process. Discrete Event Simulation Methodology is used to develop the precast-component manufacturing simulation model. A proposed GA model is developed to be integrated with the developed simulation model. This type of integration will add more intelligence to the simulation model for a better search. GA operators were developed to suit this type of problems. A precast concrete manufacturing system producing sleepers was chosen as a test bed for the proposed SIM-Crew allocation system. SIM-Crew system is easy to use and can be utilised for large scale manufacturing systems. Based on this research, computer simulation and genetic algorithms can be an effective combination and offer a great potential for improving productivity and saving production time and cost. The results showed that adopting different combinations of possible crews of workers had a substantial impact on reducing the total labour cost, process idle time and in maximising the utilisation of skilled workers.
Type:
Meetings and Proceedings; Book Chapter
Language:
en
Keywords:
allocation systems; component manufacturing; idle time; labour costs; production time; railway sleepers; engineering
Series/Report no.:
Volume 2
ISBN:
9781615673766
Rights:
No publisher policy information on http://www.sherpa.ac.uk/romeo/ [Accessed 29/03/2010]
Citation Count:
0 [Scopus, 29/03/2010]

Full metadata record

DC FieldValue Language
dc.contributor.authorAl-Bazi, A. F. J. (Ammar)en
dc.contributor.authorDawood, N. N. (Nashwan)en
dc.date.accessioned2010-03-29T13:46:55Z-
dc.date.available2010-03-29T13:46:55Z-
dc.date.issued2009-10-
dc.identifier.isbn9781615673766-
dc.identifier.urihttp://hdl.handle.net/10149/95151-
dc.description.abstractThis paper presents an innovative approach for best allocation of crews of workers on production processes by combining flow-chart based simulation tool with a powerful genetic optimization procedure. The proposed approach determines the least costly and most productive crew allocation on a set of production processes. IDEFO diagrams were developed to provide generic and functional diagrams for each process. Discrete Event Simulation Methodology is used to develop the precast-component manufacturing simulation model. A proposed GA model is developed to be integrated with the developed simulation model. This type of integration will add more intelligence to the simulation model for a better search. GA operators were developed to suit this type of problems. A precast concrete manufacturing system producing sleepers was chosen as a test bed for the proposed SIM-Crew allocation system. SIM-Crew system is easy to use and can be utilised for large scale manufacturing systems. Based on this research, computer simulation and genetic algorithms can be an effective combination and offer a great potential for improving productivity and saving production time and cost. The results showed that adopting different combinations of possible crews of workers had a substantial impact on reducing the total labour cost, process idle time and in maximising the utilisation of skilled workers.en
dc.language.isoenen
dc.publisherCurran Associates, Inc.en
dc.relation.ispartofseriesVolume 2-
dc.relation.urlhttp://www.proceedings.com/06035.htmlen
dc.rightsNo publisher policy information on http://www.sherpa.ac.uk/romeo/ [Accessed 29/03/2010]en
dc.subjectallocation systemsen
dc.subjectcomponent manufacturingen
dc.subjectidle timeen
dc.subjectlabour costsen
dc.subjectproduction timeen
dc.subjectrailway sleepersen
dc.subjectengineeringen
dc.titleAn intelligent crew allocation system for the precast manufacturing systems: Railway sleepers precast concrete as a case studyen
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, annual conference - Canadian society for civil engineeringen
dc.identifier.conferenceCanadian society for civil engineering annual conference, 2009, St. Johns, Newfoundland, Canada, May 27 - 30, 2009en
ref.citationcount0 [Scopus, 29/03/2010]en
or.citation.harvardAl-Bazi, A. F. J. and Dawood, N. N. (2009) 'An intelligent crew allocation system for the precast manufacturing systems: Railway sleepers precast concrete as a case study', Canadian society for civil engineering annual conference, St. Johns, Canada, May 27 - 30, in Proceedings, annual conference - Canadian society for civil engineering; Vol. 2. Curran Associates, Inc., pp.777-786.-
prism.startingPage777-
prism.endingPage786-
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