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Article: Applying pareto ranking and niche formation to genetic algorithm-based multiobjective time-cost optimization

TitleApplying pareto ranking and niche formation to genetic algorithm-based multiobjective time-cost optimization
Authors
KeywordsAdaptive Systems
Algorithms
Construction Costs
Construction Management
Optimization
Scheduling
Time Factors
Issue Date2005
PublisherAmerican Society of Civil Engineers. The Journal's web site is located at http://www.pubs.asce.org/journals/co.html
Citation
Journal of Construction Engineering and Management, 2005, v. 131 n. 1, p. 81-91 How to Cite?
AbstractTime-cost optimization (TCO) is one of the greatest challenges in construction project planning and control, since the optimization of either time or cost, would usually be at the expense of the other. Although the TCO problem has been extensively examined, many research studies only focused on minimizing the total cost for an early completion. This does not necessarily convey any reward to the contractor. However, with the increasing popularity of alternative project delivery systems, clients and contractors are more concerned about the combined benefits and opportunities of early completion as well as cost savings. In this paper, a genetic algorithms (GAs)-driven multiobjective model for TCO is proposed. The model integrates the adaptive weight to balance the priority of each objective according to the performance of the previous "generation." In addition, the model incorporates Pareto ranking as a selection criterion and the niche formation techniques to improve popularity diversity. Based on the proposed framework, a prototype system has been developed in Microsoft Project for testing with a medium-sized project. The results indicate that greater robustness can be attained by the introduction of adaptive weight approach, Pareto ranking, and niche formation to the GA-based multiobjective TCO model.
Persistent Identifierhttp://hdl.handle.net/10722/150265
ISSN
2015 Impact Factor: 1.152
2015 SCImago Journal Rankings: 1.219
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorZheng, DXMen_US
dc.contributor.authorNg, TSTen_US
dc.contributor.authorKumaraswamy, MMen_US
dc.date.accessioned2012-06-26T06:02:53Z-
dc.date.available2012-06-26T06:02:53Z-
dc.date.issued2005en_US
dc.identifier.citationJournal of Construction Engineering and Management, 2005, v. 131 n. 1, p. 81-91en_US
dc.identifier.issn0733-9364en_US
dc.identifier.urihttp://hdl.handle.net/10722/150265-
dc.description.abstractTime-cost optimization (TCO) is one of the greatest challenges in construction project planning and control, since the optimization of either time or cost, would usually be at the expense of the other. Although the TCO problem has been extensively examined, many research studies only focused on minimizing the total cost for an early completion. This does not necessarily convey any reward to the contractor. However, with the increasing popularity of alternative project delivery systems, clients and contractors are more concerned about the combined benefits and opportunities of early completion as well as cost savings. In this paper, a genetic algorithms (GAs)-driven multiobjective model for TCO is proposed. The model integrates the adaptive weight to balance the priority of each objective according to the performance of the previous "generation." In addition, the model incorporates Pareto ranking as a selection criterion and the niche formation techniques to improve popularity diversity. Based on the proposed framework, a prototype system has been developed in Microsoft Project for testing with a medium-sized project. The results indicate that greater robustness can be attained by the introduction of adaptive weight approach, Pareto ranking, and niche formation to the GA-based multiobjective TCO model.en_US
dc.languageengen_US
dc.publisherAmerican Society of Civil Engineers. The Journal's web site is located at http://www.pubs.asce.org/journals/co.htmlen_US
dc.relation.ispartofJournal of Construction Engineering and Managementen_US
dc.rightsJournal of Construction Engineering and Management. Copyright © American Society of Civil Engineers.-
dc.subjectAdaptive Systemsen_US
dc.subjectAlgorithmsen_US
dc.subjectConstruction Costsen_US
dc.subjectConstruction Managementen_US
dc.subjectOptimizationen_US
dc.subjectSchedulingen_US
dc.subjectTime Factorsen_US
dc.titleApplying pareto ranking and niche formation to genetic algorithm-based multiobjective time-cost optimizationen_US
dc.typeArticleen_US
dc.identifier.emailNg, TST: tstng@hkucc.hku.hken_US
dc.identifier.emailKumaraswamy, MM: mohan@hkucc.hku.hken_US
dc.identifier.authorityNg, ST=rp00158en_US
dc.identifier.authorityKumaraswamy, MM=rp00126en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1061/(ASCE)0733-9364(2005)131:1(81)en_US
dc.identifier.scopuseid_2-s2.0-12344265028en_US
dc.identifier.hkuros102516-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-12344265028&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume131en_US
dc.identifier.issue1en_US
dc.identifier.spage81en_US
dc.identifier.epage91en_US
dc.identifier.isiWOS:000225896800009-
dc.publisher.placeUnited Statesen_US
dc.identifier.scopusauthoridZheng, DXM=7202567393en_US
dc.identifier.scopusauthoridNg, ST=7403358853en_US
dc.identifier.scopusauthoridKumaraswamy, MM=35566270600en_US

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