Intelligence approach to production planning system for bespoke precast concrete products

Hdl Handle:
http://hdl.handle.net/10149/98134
Title:
Intelligence approach to production planning system for bespoke precast concrete products
Authors:
Benjaoran, V. (Vacharapoom); Dawood, N. N. (Nashwan)
Affiliation:
University of Teesside. Centre for Construction Innovation Research.
Citation:
Benjaoran, V. and Dawood, N. N. (2006) 'Intelligence approach to production planning system for bespoke precast concrete products', Automation in Construction, 15 (6), pp.737-745.
Publisher:
Elsevier
Journal:
Automation in Construction
Issue Date:
Nov-2006
URI:
http://hdl.handle.net/10149/98134
DOI:
10.1016/j.autcon.2005.09.007
Abstract:
Bespoke precast concrete products are widely used components of construction projects. These products implement the offsite prefabrication technology that offers a unique opportunity for innovation and cost savings for construction projects. However, the production process from design to manufacturing contains uncertainties due to external factors: multi-disciplinary design, progress on construction site. The typical workload on bespoke precast factories is a complex combination of uniquely and identically designed products, which have various delivery dates and requirement of costly purpose-built moulds. In this context, this research is aimed to improve the efficiency of the process by addressing the production planning because it has a significant impact to the success of the production programme. An innovative planning system and its prototype called 'Artificial Intelligence Planner' (AIP) are developed. AIP is capable of two functionalities. The first is a data integration system that encourages the automation in the planning process. The other is a decision support system for planners to improve the efficiency of the production plans. These functionalities reinforce each other to deliver optimum benefits to precast manufacturers. AIP have employed artificial intelligence technologies: neural network and genetic algorithm to enhance data analyses for being a decision support for production planning. The outcomes of the research include shortened customer lead-time, in-house repository of production knowledge, and achievement of the optimum factory's resource utilisation.
Type:
Article
Language:
en
Keywords:
artificial intelligence; offsite prefabrication; pre-cast concrete products; production planning
ISSN:
0926-5805
Rights:
Author can archive post-print (ie final draft post-refereeing). For full details see http://www.sherpa.ac.uk/romeo/ [Accessed 07/05/2010]
Citation Count:
0 [Scopus, 07/05/2010]

Full metadata record

DC FieldValue Language
dc.contributor.authorBenjaoran, V. (Vacharapoom)en
dc.contributor.authorDawood, N. N. (Nashwan)en
dc.date.accessioned2010-05-07T08:47:56Z-
dc.date.available2010-05-07T08:47:56Z-
dc.date.issued2006-11-
dc.identifier.citationAutomation in Construction; 15(6):737-745en
dc.identifier.issn0926-5805-
dc.identifier.doi10.1016/j.autcon.2005.09.007-
dc.identifier.urihttp://hdl.handle.net/10149/98134-
dc.description.abstractBespoke precast concrete products are widely used components of construction projects. These products implement the offsite prefabrication technology that offers a unique opportunity for innovation and cost savings for construction projects. However, the production process from design to manufacturing contains uncertainties due to external factors: multi-disciplinary design, progress on construction site. The typical workload on bespoke precast factories is a complex combination of uniquely and identically designed products, which have various delivery dates and requirement of costly purpose-built moulds. In this context, this research is aimed to improve the efficiency of the process by addressing the production planning because it has a significant impact to the success of the production programme. An innovative planning system and its prototype called 'Artificial Intelligence Planner' (AIP) are developed. AIP is capable of two functionalities. The first is a data integration system that encourages the automation in the planning process. The other is a decision support system for planners to improve the efficiency of the production plans. These functionalities reinforce each other to deliver optimum benefits to precast manufacturers. AIP have employed artificial intelligence technologies: neural network and genetic algorithm to enhance data analyses for being a decision support for production planning. The outcomes of the research include shortened customer lead-time, in-house repository of production knowledge, and achievement of the optimum factory's resource utilisation.en
dc.language.isoenen
dc.publisherElsevieren
dc.rightsAuthor can archive post-print (ie final draft post-refereeing). For full details see http://www.sherpa.ac.uk/romeo/ [Accessed 07/05/2010]en
dc.subjectartificial intelligenceen
dc.subjectoffsite prefabricationen
dc.subjectpre-cast concrete productsen
dc.subjectproduction planningen
dc.titleIntelligence approach to production planning system for bespoke precast concrete productsen
dc.typeArticleen
dc.contributor.departmentUniversity of Teesside. Centre for Construction Innovation Research.en
dc.identifier.journalAutomation in Constructionen
ref.citationcount0 [Scopus, 07/05/2010]en
or.citation.harvardBenjaoran, V. and Dawood, N. N. (2006) 'Intelligence approach to production planning system for bespoke precast concrete products', Automation in Construction, 15 (6), pp.737-745.-
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