Decision support model for incentives/disincentives time–cost tradeoff

Offering contractors a monetary incentive for early project completion provides agencies with an innovative means to expedite construction. To be effective, the incentive amount should exceed the contractor's additional cost (CAC) for completing the project early. Yet, estimating CAC poses a major challenge to agencies largely because of contractors' reluctance to disclose information about their profits. This study introduces a predictive, quantitative model that estimates realistic CACs by combining an existing schedule simulation technique with a regression method. An innovative, reliable tool called Construction Analysis for Pavement Rehabilitation Strategies (CA4PRS) was used for the simulation. Using CA4PRS, a set of contractors' time–cost tradeoff data was created and a linear regression analysis based on a second degree polynomial equation was performed to predict CAC growth rate by analyzing how the CAC interacts with the agency's specified schedule goal. The robustness of the proposed model was then validated through two case studies. This model can assist decision-makers in estimating better optimal incentive amounts.

► We develop a stochastic model that estimates the contractor's additional cost (CAC).
► The CAC serves as the lower bound of incentive/disincentive (I/D) for highway construction projects.
► Two case studies validate the robustness of the proposed model.
► Use of the model will directly impact current ad-hoc practices for determining I/D rates.