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 contractorʹs additional cost (CAC) for completing the project early. Yet, estimating CAC poses a major challenge to agencies largely because of contractorsʹ 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 contractorsʹ 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 agencyʹs 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.