Developing a model for construction contractors pre-qualification in the Gaza Strip and West Bank

Abstract

Contractor pre-qualification is characterised as a multi-criteria problem with uncertain inputs. The criteria used for pre-qualification includes qualitative and quantitative information. Owing to the nature of pre-qualification, which depends on subjective judgements of construction professionals, it becomes an art rather than a science. Two approaches are found in the literature to model the contractor's pre-qualification criteria; Linear and non-linear models. The main aim of this research is to offer a rational method for contractor prequalification that enables to pre-qualify the contractors who are able to achieve the client's objectives. The main question guiding the research is how to be sure that the selected contractor is able to achieve the client's objectives. It is believed that there is an indirect relationship between the contractor's attributes and the contractor's ability to achieve the client's objectives. The time, cost and quality overruns of a project have been used as indicators to measure the contractor's ability to achieve client's objectives. To achieve this aim, the methodologies used included literature review, questionnaires, surveys, and hypothetical and real-life case studies. This work suggested improvements to the previous contractor pre-qualification models by using a hybrid model, combining the merits of Analytical Hierarchy Process (AHP), Neural Networks (NN) and Genetic Algorithms (GA) in one consolidated model called the Genetic Neural Network (GNN) model. AHP was used to establish relative weights of the contractor's pre-qualification criteria; NN was used as the main processing tool to find a relationship between the contractor's …