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

K.C. Lam · S. Thomas Ng · Martin Skitmore · Sai On Cheung

67 citations0/0 files storedEngineering Construction & Architectural Management

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Construction Project Management and Performance Multi-Criteria Decision Making Public Procurement and Policy

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.

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References

1980The analytic hierarchy process : planning, priority setting, resource allocation 1993Feedforward Neural Nets as Models for Time Series Forecasting 1997Criteria for contractor selection 1998Contractor selection using multicriteria utility theory: An additive model 1988Decision Criteria in Contractor Prequalification 1991Time series forecasting using backpropagation neural networks 1995Back propagation in time‐series forecasting 1985Tender Evaluation by Fuzzy Sets

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2010Contractor selection innovation: examination of two decades' published research 2012The impact of contractors' prequalification on construction project delivery in Nigeria 2008Contractor selection: fuzzy-control approach 2017A type-2 fuzzy set model for contractor prequalification 2016A multi-agent systems-based contractor pre-qualification model 2023AI-based decision support system for public procurement

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