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Article: Applying a genetic algorithm-based multiobjective approach for time-cost optimization

TitleApplying a genetic algorithm-based multiobjective approach for time-cost optimization
Authors
KeywordsAlgorithms
Cost control
Optimization
Project management
Time factors
Issue Date2004
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, 2004, v. 130 n. 2, p. 168-176 How to Cite?
AbstractReducing both project cost and time (duration) is critical in a competitive environment. However, a trade-off between project time and cost is required. This in turn requires contracting organizations to carefully evaluate various approaches to attaining an optimal time-cost equilibrium. Although several analytical models have been developed for time-cost optimization (TCO), they mainly focus on projects where the contract duration is fixed. The optimization objective in those cases is therefore restricted to identifying the minimum total cost only. With the increasing popularity of alternative project delivery systems, clients and contractors are targeting the increased benefits and opportunities of seeking an earlier project completion. The multiobjective model for TCO proposed in this paper is powered by techniques using genetic algorithms (GAs). The proposed model integrates the adaptive weights derived from previous generations, and induces a search pressure toward an ideal point. The concept of the GA-based multiobjective TCO model is illustrated through a simple manual simulation, and the results indicate that the model could assist decision-makers in concurrently arriving at an optimal project duration and total cost.
Persistent Identifierhttp://hdl.handle.net/10722/150311
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.accessioned2012-06-26T06:03:13Z-
dc.date.available2012-06-26T06:03:13Z-
dc.date.issued2004en_US
dc.identifier.citationJournal of Construction Engineering and Management, 2004, v. 130 n. 2, p. 168-176en_US
dc.identifier.issn0733-9364en_US
dc.identifier.urihttp://hdl.handle.net/10722/150311-
dc.description.abstractReducing both project cost and time (duration) is critical in a competitive environment. However, a trade-off between project time and cost is required. This in turn requires contracting organizations to carefully evaluate various approaches to attaining an optimal time-cost equilibrium. Although several analytical models have been developed for time-cost optimization (TCO), they mainly focus on projects where the contract duration is fixed. The optimization objective in those cases is therefore restricted to identifying the minimum total cost only. With the increasing popularity of alternative project delivery systems, clients and contractors are targeting the increased benefits and opportunities of seeking an earlier project completion. The multiobjective model for TCO proposed in this paper is powered by techniques using genetic algorithms (GAs). The proposed model integrates the adaptive weights derived from previous generations, and induces a search pressure toward an ideal point. The concept of the GA-based multiobjective TCO model is illustrated through a simple manual simulation, and the results indicate that the model could assist decision-makers in concurrently arriving at an optimal project duration and total cost.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.subjectAlgorithmsen_US
dc.subjectCost controlen_US
dc.subjectOptimizationen_US
dc.subjectProject managementen_US
dc.subjectTime factorsen_US
dc.titleApplying a genetic algorithm-based multiobjective approach for 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_OA_fulltexten_US
dc.identifier.doi10.1061/(ASCE)0733-9364(2004)130:2(168)en_US
dc.identifier.scopuseid_2-s2.0-2342615793en_US
dc.identifier.hkuros91794-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-2342615793&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume130en_US
dc.identifier.issue2en_US
dc.identifier.spage168en_US
dc.identifier.epage176en_US
dc.identifier.isiWOS:000220571500002-
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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