Contractor prequalification using fuzzy sets

David J. Elton, C. Hsein Juang, Jeffrey S. Russell

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Contractor prequalification involves evaluation of factors that contain a high degree of uncertainty. Current methods of contractor prequalification fail to address uncertainty in the evaluation process. The use of fuzzy sets is examined to address this shortcoming. Fuzzy sets have been employed in order to provide a more consistent, rational method of evaluating the non-random uncertainties that are present in the contractor evaluation process. The linguistic factors (such as good, fair, or poor) used in fuzzy set analysis provide a more appropriate means to model decision circumstances that occur in industry than the current crisp (usually numerical) factors. Evaluation factors from a previous study are used. Each factor was assigned a grade. Correspondingly, each grade was represented by a fuzzy set. These fuzzy sets were then weighted and combined resulting in a fuzzy set. This composite fuzzy set represents the contractor's rating. When evaluating multiple contractors, each contractor's composite rating is rank ordered to allow the project owner to select the most desirable contractors to participate in the bidding process.

Original languageEnglish
Pages (from-to)1-17
Number of pages17
JournalCivil Engineering Systems
Volume11
Issue number1
DOIs
StatePublished - Jan 1994

Keywords

  • Contractor
  • Contractor Prequalification Rating Index
  • CPRI
  • fuzzy sets
  • prequalification
  • ranking
  • selection
  • uncertainty

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