Development of a decision support system (DSS) for the contractors decision to bid: regression analysis and neural network solutions

The decision whether to bid or not for a project is extremely important to contractors; besides the issues of resource allocation, the preparation of a bona fide tender commits the organisation to considerable expenditure, which is only recovered if the bid is successful. There is, therefore, a potential financial benefit to be realised through the adoption of an effective and systematic approach to the decision to bid process. Artificial neural network and regression techniques are used to produce a rational and optimal model for the bid/no-bid decision process. While the regression model is ultimately rejected, the selected back-propagation network, comprising 21 input nodes, 3 hidden layers and 4 output nodes is used to support a DSS for the decision to bid process. The results obtained demonstrate that the model functions effectively in predicting the decision process.

[1]  Ali A. Minai,et al.  Back-propagation heuristics: a study of the extended delta-bar-delta algorithm , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[2]  Chris Chapman,et al.  Developing Competitive Bids: A Framework For Information Processing , 1988 .

[3]  Tarek Hegazy,et al.  NEURAL NETWORK MODEL FOR PARAMETRIC COST ESTIMATION OF HIGHWAY PROJECTS , 1998 .

[4]  Miroslaw J. Skibniewski,et al.  Estimating construction productivity: neural-network-based approach , 1994 .

[5]  Martin Skitmore,et al.  An introduction to bidding strategy , 1992 .

[6]  David Lowe,et al.  Development of a decision support system (DSS) for the contractor's decision to bid: regression and neural networks solutions , 2004 .

[7]  Simaan M. AbouRizk,et al.  UTILITY-THEORY MODEL FOR BID MARKUP DECISIONS , 1996 .

[8]  M. Gates Bidding Strategies and Probabilities , 1967 .

[9]  Robert I. Carr,et al.  General Bidding Model , 1982 .

[10]  William R. Park,et al.  Construction Bidding: Strategic Pricing for Profit , 1992 .

[11]  David Lowe,et al.  Data modelling and the application of a neural network approach to the prediction of total construction costs , 2002 .

[12]  Mohammed Fadhil Dulaimi,et al.  The factors influencing bid mark-up decisions of large- and medium-size contractors in Singapore , 2002 .

[13]  Mohammed Wanous,et al.  To bid or not to bid: a parametric solution , 2000 .

[14]  Parvar Jamshid,et al.  Development of a Decision Support Model to Inform an Organization's Marketing and 'Decision to Bid' Strategies , 2000 .

[15]  Mohamed Marzouk,et al.  A decision support tool for construction bidding , 2003 .

[16]  Adrian J. Smith Estimating, Tendering and Bidding for Construction , 1995 .

[17]  Irtishad Ahmad,et al.  Questionnaire Survey on Bidding in Construction , 1988 .

[18]  R. Fellows,et al.  An examination of the importance of resource considerations when contractors make project selection decisions , 1992 .

[19]  Ali A. Shash,et al.  SUBCONTRACTORS' BIDDING DECISIONS , 1998 .

[20]  L. Friedman A Competitive-Bidding Strategy , 1956 .

[21]  Peter E.D. Love,et al.  Combining rule-based expert systems and artificial neural networks for mark-up estimation , 1999 .

[22]  A. H. Boussabaine,et al.  Modelling cost‐flow forecasting for water pipeline projects using neural networks , 1999 .

[23]  Tarek Hegazy,et al.  Neural networks as tools in construction , 1991 .

[24]  A. Shash Factors considered in tendering decisions by top UK contractors , 1993 .

[25]  D. K. H. Chua,et al.  Key Factors in Bid Reasoning Model , 2000 .