Decision support system for contractor pre‐qualification—artificial neural network model

Abstract

The selection criteria for contractor pre‐qualification are characterized by the co‐existence of both quantitative and qualitative data. The qualitative data is non‐linear, uncertain and imprecise. An ideal decision support system for contractor pre‐qualification should have the ability of handling both quantitative and qualitative data, and of mapping the complicated non‐linear relationship of the selection criteria, such that rational and consistent decisions can be made. In this research paper, an artificial neural network model was developed to assist public clients identifying suitable contractors for tendering. The pre‐qualification criteria (variables) were identified for the model. One hundred and twelve real pre‐qualification cases were collected from civil engineering projects in Hong Kong, and 88 hypothetical pre‐qualification cases were also generated according to the ‘If‐then’ rules used by professionals in the pre‐qualification process. The results of the analysis totally comply with current practice (public developers in Hong Kong). Each pre‐qualification case consisted of input ratings for candidate contractors' attributes and their corresponding pre‐qualification decisions. The training of the neural network model was accomplished by using the developed program, in which a conjugate gradient descent algorithm was incorporated for improving the learning performance of the network. Cross‐validation was applied to estimate the generalization errors based on the ‘re‐sampling’ of training pairs. The case studies show that the artificial neural network model is suitable for mapping the complicated non‐linear relationship between contractors' attributes and their corresponding pre‐qualification (disqualification) decisions. The artificial neural network model can be concluded as an ideal alternative for performing the contractor pre‐qualification task.

A Group Decision-Making Aggregation Model for Contractor Selection in Large Scale Construction Projects Based on Two-Stage Partial Least Squares (PLS) Path Modeling

“…A series of decision models based on various approaches were developed for contractor selection, such as the integrated multi-objective decision making process (Keeney and Raiffa 1976;Kashiwagi and Byfield 2002;Demirtas and Üstün 2008), the neural networks (Khosrowshahi 2001;Lam et al 2000), AHP (Al-Harbi 2001;Al-Reshaid and Kartam 2005;Mahdi et al 2002;Topcu 2004), DEA (Tran 2002;McCabe et al 2005) the multi-attribute analysis model (Lai et al 2004), analytic network process (Cheng and Li 2004;Ravi et al 2005;Bayazit 2006), integer programming (Missbauer and Hauber 2005), unit-price based (Wang et al 2006), multi-attribute utility theory (Lambropoulos 2007), fuzzy-excellent order method (Wang and Triantaphyllou 2008), fuzzy-AHP-Smart (Padhi and Mohapatra 2009), and the binary goal programming model (Padhi et al 2010). Although these methods were designed to single decision maker, they have drawn great attention to the area of contractor selection in construction projects and provided basis and new ideas to group decision-making.…”

A support vector machine model for contractor prequalification

“…[24,26,27] identified some potential applications of artificial neural networks for contractor prequalification problems. For example, [26] presented a case study of neural network application for contractor prequalification that used practical datasets from UK based local authority project procurements.…”

“…Consequently, numerous models/systems ranging from systematic linear frameworks [12,17,[19][20][21] to complicated artificial intelligence applications and decision support systems were conceived to enhance the prequalification decision-making, e.g. [22][23][24][25][26][27][28][29]. However, due to the inherent complexities such as multi-criteria considerations, precise mathematical models are seldom considered for the contractor selection problems.…”

A multiple kernel learning-based decision support model for contractor pre-qualification

“…It involves some problems such as subjectivity, non-linearity, and multi-criteria. Subjectivity means that the decisions are depending on the intuitive judgments of the decision makers; non-linearity may be caused by the non-linear relationship between the score of the individual criterion and its impact to the decision to be made, as well as the different weights borne by the individual criterion; for multi-criteria, it reflects the existence of a large number of decision criteria that may or may not be at the same level [1][2][3]. In practice, the contractors' suitability to participate in a project bid is usually assessed by the project owners in accordance with their previous experience, judgment and a set of criteria which might vary between projects and clients.…”