Contractor prequalification model using fuzzy sets

Abstract Contractor prequalification makes it possible to admit for tendering only competent contractors. The undertaken decisions demand taking into consideration many criteria, including among others, experience and financial standing of the candidates. It is often difficult to be quantified. The objectives of the construction owner in a given project are also meaningful. All these factors cause difficulties in working out a mathematical prequalification model. In the paper a model based on fuzzy sets theory is proposed. It takes into consideration both different criteria, objectives and evaluations of numerous decision ‐ makers. To illustrate the model operation a simple numerical example is presented.

[1]  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 .

[2]  Ekambaram Palaneeswaran,et al.  Web-Based Client Advisory Decision Support System for Design.Builder Prequalification , 2005 .

[3]  Edmundas Kazimieras Zavadskas,et al.  Multi-Attribute Decision-Making Model by Applying Grey Numbers , 2009, Informatica.

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

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

[6]  Jeffrey S. Russell,et al.  Decision models for analysis and evaluation of construction contractors , 1992 .

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

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

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

[10]  Ka Chi Lam,et al.  Using the principal component analysis method as a tool in contractor pre‐qualification , 2005 .

[11]  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 .

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

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

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

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

[16]  Wei-Chih Wang,et al.  Improving AHP for construction with an adaptive AHP approach (A3) , 2008 .

[17]  Jakrapong Pongpeng,et al.  TenSeM: a multicriteria and multidecision-makers' model in tender evaluation , 2003 .

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

[19]  Seung Heon Han,et al.  A web-based integrated system for international project risk management , 2008 .

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

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

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

[23]  Jakrapong Pongpeng,et al.  A multicriteria model's survey: state of the art and some necessary capabilities of future models , 2003 .

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

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

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

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

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

[29]  Jeffrey S. Russell,et al.  QUALIFIER-1: CONTRACTOR PREQUALIFICATION MODEL , 1990 .

[30]  Zedan Hatush,et al.  Evaluating contractor prequalification data: selection criteria and project success factors , 1997 .

[31]  Martin Skitmore,et al.  Assessment and evaluation of contractor data against client goals using PERT approach , 1997 .

[32]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

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

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

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

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