A fuzzy sets based contractor prequalification procedure

Abstract Contractor prequalification makes it possible to admit for tendering only competent contractors. The paper presents a proposal for contractor prequalification schema involving two stages of prequalification: “on a standing list” and “per project”. A model of prequalification employing the theory of fuzzy sets to evaluate the “per project” contractors is precisely described. A simple numerical example illustrates the model operation and a description of a program supporting the prequalification procedure follows.

[1]  Aminah Robinson Fayek,et al.  Automated Corrective Action Selection Assistant , 1994 .

[2]  Edmundas Kazimieras Zavadskas,et al.  Multicriteria evaluation of apartment blocks maintenance contractors: Lithuanian case study , 2009 .

[3]  E. Zavadskas,et al.  Multi‐objective contractor's ranking by applying the Moora method , 2008 .

[4]  Edyta Plebankiewicz,et al.  Contractor prequalification model using fuzzy sets , 2010 .

[5]  Sigitas Mitkus,et al.  Analysis of criteria system model for construction contract evaluation , 2007 .

[6]  Valentinas Podvezko,et al.  Multicriteria graphical‐analytical evaluation of the financial state of construction enterprises , 2008 .

[7]  George J. Klir,et al.  Fuzzy sets, uncertainty and information , 1988 .

[8]  Gary David Holt,et al.  Prequalification and multi-criteria selection: a measure of contractors' opinions , 1998 .

[9]  A. Kaufmann,et al.  Introduction to fuzzy arithmetic : theory and applications , 1986 .

[10]  Edyta Plebankiewicz.  Construction contractor prequalification from polish clients’ perspective , 2010 .

[11]  Robert L. K. Tiong,et al.  Contractor Selection Criteria: Investigation of Opinions of Singapore Construction Practitioners , 2006 .

[12]  Oleg Kapliński,et al.  Game theory applications in construction engineering and management , 2010 .

[13]  Sigitas Mitkus,et al.  Reasoned decisions in construction contracts evaluation , 2008 .

[14]  Yawei Li,et al.  Fuzzy Approach to Prequalifying Construction Contractors , 2007 .

[15]  Edmundas Kazimieras Zavadskas,et al.  A new additive ratio assessment (ARAS) method in multicriteria decision‐making , 2010 .

[16]  Edmundas Kazimieras Zavadskas,et al.  A multiple criteria evaluation of multi-family apartment block's maintenance contractors: I—Model for maintenance contractor evaluation and the determination of its selection criteria , 2006 .

[17]  Edmundas Kazimieras Zavadskas,et al.  Contractor selection for construction works by applying saw‐g and topsis grey techniques , 2010 .

[18]  S. Thomas Ng,et al.  A fuzzy gap analysis model for evaluating the performance of engineering consultants , 2007 .

[19]  Ashraf Elazouni.  Classifying construction contractors using unsupervised-learning neural networks , 2006 .

[20]  Van Uu Nguyen,et al.  Tender evaluation by fuzzy sets , 1985 .

[21]  Zenonas Turskis,et al.  Multi‐attribute contractors ranking method by applying ordering of feasible alternatives of solutions in terms of preferability technique , 2008 .

[22]  Edmundas Kazimieras Zavadskas,et al.  Multiple criteria analysis of foundation instalment alternatives by applying Additive Ratio Assessment (ARAS) method , 2010 .

[23]  Michael Riley,et al.  A multi‐criteria approach to contractor selection , 2002 .

[24]  E. Plebankiewicz,et al.  Simple Prequalification Models , 2010 .

[25]  Farzad Khosrowshahi,et al.  Neural network model for contractors’ prequalification for local authority projects , 1999 .

[26]  S. Thomas Ng,et al.  EQUAL: a case-based contractor prequalifier , 2001 .

[27]  Patrick S. W. Fong,et al.  Final contractor selection using the analytical hierarchy process , 2000 .

[28]  Edmundas Kazimieras Zavadskas,et al.  Contractor selection of construction in a competitive environment , 2008 .

[29]  Edmundas Kazimieras Zavadskas,et al.  Multi-attribute Assessment of Road Design Solutions by Using the COPRAS Method , 2007 .

[30]  Jeffrey S. Russell,et al.  Contractor prequalification data for construction owners , 1992 .

[31]  J. Bröchner,et al.  Price and Nonprice Criteria for Contractor Selection , 2006 .

[32]  E. Zavadskas,et al.  Multiple criteria decision making (MCDM) methods in economics: an overview , 2011 .

[33]  Miroslaw J. Skibniewski,et al.  Qualifier-2: Knowledge-Based System for Contractor Prequalification , 1990 .

[34]  Ekambaram Palaneeswaran,et al.  A support vector machine model for contractor prequalification , 2009 .

[35]  Tiesong Hu,et al.  A fuzzy neural network approach for contractor prequalification , 2001 .

[36]  Kamal M. Al‐Subhi Al‐Harbi,et al.  Application of the AHP in project management , 2001 .

[37]  Edmundas Kazimieras Zavadskas,et al.  Multi-Attribute Decision-Making Methods for Assessment of Quality in Bridges and Road Construction: State-Of-The-Art Surveys , 2008 .

[38]  Caroline M. Eastman,et al.  Response: Introduction to fuzzy arithmetic: Theory and applications : Arnold Kaufmann and Madan M. Gupta, Van Nostrand Reinhold, New York, 1985 , 1987, Int. J. Approx. Reason..

[39]  Robert L. K. Tiong,et al.  A Fuzzy Decision Framework for Contractor Selection , 2005 .