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Article: Stochastic time-cost optimization model incorporating fuzzy sets theory and nonreplaceable front

TitleStochastic time-cost optimization model incorporating fuzzy sets theory and nonreplaceable front
Authors
KeywordsAlgorithms
Cost Control
Fuzzy Sets
Productivity
Project Management
Risk Management
Stochastic Processes
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. 2, p. 176-186 How to Cite?
AbstractIn a real construction project, the duration and cost of each activity could change dynamically as a result of many uncertain variables, such as weather, resource availability, productivity, etc. Managers/planners must take these uncertainties into account and provide an optimal balance of time and cost based on their own experience and knowledge. In this paper, fuzzy sets theory is applied to model the managers' behavior in predicting time and cost pertinent to a specific option within an activity. Genetic algorithms are used as a searching mechanism to establish the optimal time-cost profiles under different risk levels. In addition, the nonreplaceable front concept is proposed to assist managers in recognizing promising solutions from numerous candidates on the Pareto front. Economic analysis skills, such as the utility theory and opportunity cost, are integrated into the new model to mimic the decision making process of human experts. A simple case study is used for testing the new model developed. In comparison with the previous models, the new model provides managers with greater flexibility to analyze their decisions in a more realistic manner. The results also indicate that greater robustness may be achieved by taking some risks. This research is relevant to both industry practitioners and researchers. By incorporating the concept of fuzzy sets, managers can represent the range of possible time-cost values as well as their associated degree of belief. The model presented in this paper can, therefore, support decision makers in analyzing their time-cost optimization decision in a more flexible and realistic manner. Many novel ideas have also been incorporated in this paper to benefit the research community. Examples of these include the use of fuzzy sets theory, nonreplaceable front concept, utility theory, opportunity cost, etc. With suitable modifications, these concepts can be applied to model to other similar optimization problems in construction.
Persistent Identifierhttp://hdl.handle.net/10722/150268
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.date.accessioned2012-06-26T06:02:54Z-
dc.date.available2012-06-26T06:02:54Z-
dc.date.issued2005en_US
dc.identifier.citationJournal Of Construction Engineering and Management, 2005, v. 131 n. 2, p. 176-186en_US
dc.identifier.issn0733-9364en_US
dc.identifier.urihttp://hdl.handle.net/10722/150268-
dc.description.abstractIn a real construction project, the duration and cost of each activity could change dynamically as a result of many uncertain variables, such as weather, resource availability, productivity, etc. Managers/planners must take these uncertainties into account and provide an optimal balance of time and cost based on their own experience and knowledge. In this paper, fuzzy sets theory is applied to model the managers' behavior in predicting time and cost pertinent to a specific option within an activity. Genetic algorithms are used as a searching mechanism to establish the optimal time-cost profiles under different risk levels. In addition, the nonreplaceable front concept is proposed to assist managers in recognizing promising solutions from numerous candidates on the Pareto front. Economic analysis skills, such as the utility theory and opportunity cost, are integrated into the new model to mimic the decision making process of human experts. A simple case study is used for testing the new model developed. In comparison with the previous models, the new model provides managers with greater flexibility to analyze their decisions in a more realistic manner. The results also indicate that greater robustness may be achieved by taking some risks. This research is relevant to both industry practitioners and researchers. By incorporating the concept of fuzzy sets, managers can represent the range of possible time-cost values as well as their associated degree of belief. The model presented in this paper can, therefore, support decision makers in analyzing their time-cost optimization decision in a more flexible and realistic manner. Many novel ideas have also been incorporated in this paper to benefit the research community. Examples of these include the use of fuzzy sets theory, nonreplaceable front concept, utility theory, opportunity cost, etc. With suitable modifications, these concepts can be applied to model to other similar optimization problems in construction.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.subjectFuzzy Setsen_US
dc.subjectProductivityen_US
dc.subjectProject Managementen_US
dc.subjectRisk Managementen_US
dc.subjectStochastic Processesen_US
dc.subjectTime Factorsen_US
dc.titleStochastic time-cost optimization model incorporating fuzzy sets theory and nonreplaceable fronten_US
dc.typeArticleen_US
dc.identifier.emailNg, TST: tstng@hkucc.hku.hken_US
dc.identifier.authorityNg, ST=rp00158en_US
dc.description.naturelink_to_OA_fulltexten_US
dc.identifier.doi10.1061/(ASCE)0733-9364(2005)131:2(176)en_US
dc.identifier.scopuseid_2-s2.0-12744266507en_US
dc.identifier.hkuros102517-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-12744266507&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume131en_US
dc.identifier.issue2en_US
dc.identifier.spage176en_US
dc.identifier.epage186en_US
dc.identifier.isiWOS:000226430400004-
dc.publisher.placeUnited Statesen_US
dc.identifier.scopusauthoridZheng, DXM=7202567393en_US
dc.identifier.scopusauthoridNg, ST=7403358853en_US

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