A meta classification and analysis of contractor selection and prequalification

Faikcan Kog*, Hakan Yaman

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

20 Citations (Scopus)

Abstract

In order to attain the objectives of a construction project all of the resources should be used effectively and efficiently. Therefore, the success of a construction project is directly related to organize the project team and to select the building production process participants accurately who will use these resources. Selection of the contractor, as a main participant of a construction project, is the most important and challenging decision process for a client. The objectives of this paper are (1) to introduce the findings of the literature review about the contractor selection and prequalification models and systems studied between 1992-2013 (2) to classify academic studies dealing with pre-qualification and selection criteria of contractors using a Meta classification method. In this paper, 133 peer-reviewed academic studies have been analyzed and classified in the domain of contractor selection, contractor pre-qualification and weighting criteria. A metaclassification system is adapted and used in order to present the state of the art of contractor pre-qualification and selection challenge. It is obtained that the statistical models, fuzzy set theory and AHP are the most preferred methods in order to solve contractor selection problem. Moreover, an increment in the studies of contractor selection in the last period is determined.

Original languageEnglish
Pages (from-to)302-310
Number of pages9
JournalProcedia Engineering
Volume85
DOIs
Publication statusPublished - 2014
Event2014 Creative Construction Conference, CCC 2014 - Prague, Czech Republic
Duration: 21 Jun 201424 Jun 2014

Bibliographical note

Publisher Copyright:
© 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license.

Keywords

  • Contractor pre-qualification
  • Contractor selection
  • Meta-analysis
  • Meta-classification
  • Weighting criteria

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