Cloud Contractor Selection Model for Design-Build Open Tender

Abstract

For transparency and accountability, public sector clients typically select a contractor using lowest-price open tendering. This paper focuses on improving the selection of a design-build contractor by modeling the inherent uncertainties associated with open tendering. Although there are numerous methods for selecting a contractor, few consider, and even fewer have accounted for, random uncertainties associated with open tendering. In the absence of a structured approach that includes cognitive uncertainty modeling, the risk of choosing a subpar contractor and consequent project failure increases. Cloud theory uses a normal distribution membership function to infuse fuzzy set theory with probability theory. The application of cloud theory in a case study to order the preferred contractors improves decision-making quality and, consequently, confidence in the derived outcome where uncertainty exists. By accounting for both fuzzy and random uncertainties expressed by decision makers, this formulation for contractor selection increases the potential to achieve client performance goals. The proposed new approach reduces the risk of project failure by offering a unique understanding of how to more efficiently choose a design-build contractor. It narrows the model prediction and practice gap by providing an alternative to selecting the lowest bidder.