Please use this identifier to cite or link to this item: https://doi.org/10.1061/(ASCE)0733-9364(2001)127:1(35)
DC FieldValue
dc.titleCase-based reasoning approach in bid decision making
dc.contributor.authorChua, D.K.H.
dc.contributor.authorLi, D.Z.
dc.contributor.authorChan, W.T.
dc.date.accessioned2014-06-17T08:15:04Z
dc.date.available2014-06-17T08:15:04Z
dc.date.issued2001-01
dc.identifier.citationChua, D.K.H., Li, D.Z., Chan, W.T. (2001-01). Case-based reasoning approach in bid decision making. Journal of Construction Engineering and Management 127 (1) : 35-45. ScholarBank@NUS Repository. https://doi.org/10.1061/(ASCE)0733-9364(2001)127:1(35)
dc.identifier.issn07339364
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/65285
dc.description.abstractSince contractors' bidding behaviors are affected by numerous factors related both to the specific features of the project and dynamically changed situations, bidding decision problems are highly unstructured. No clear rules can be found in delivering a bidding decision. In this problem domain, decisions are commonly made based upon intuition and past experience. Case-based reasoning (CBR) is a subbranch of artificial intelligence. It solves new problems by matching against similar problems that have been encountered and resolved in the past. It is a useful tool in dealing with complex and unstructured problems, which are difficult if not impossible to be theoretically modeled. This paper presents a case-based reasoning bidding system that helps contractors with the dynamic information varying with the specific features of the job and the new situation. In this system, bid cases are represented by sets of attributes derived from a preliminary survey of several experienced bidders, focusing, respectively, on two reasoning subgoals: (1) Risk; and (2) competition. Through the system, similar cases can be retrieved to assess the possible level of competition and risk margin. A hypothetical example is explained and evaluated to demonstrate the feasibility of the method. The effectiveness of this system is tested by a Monte Carlo simulation in comparison to the conventional statistical method.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1061/(ASCE)0733-9364(2001)127:1(35)
dc.sourceScopus
dc.typeArticle
dc.contributor.departmentCIVIL ENGINEERING
dc.description.doi10.1061/(ASCE)0733-9364(2001)127:1(35)
dc.description.sourcetitleJournal of Construction Engineering and Management
dc.description.volume127
dc.description.issue1
dc.description.page35-45
dc.description.codenJCEMD
dc.identifier.isiut000166597300004
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