Stochastic Modeling for Quantifying Optimal Incentive Amounts of Early Project Completion

An effective Incentive/Disincentive (I/D) rate should fall above what a contractor needs to turn a profit by exceeding Contractor’s Additional Cost (CAC) for expediting construction and below the total cost saving by the agency. However, determining this type of I/D rate has been extremely challenging because of contractor’s reluctance to disclose pertinent CAC data and state transportation agencies’ lack of a systematic method for determining I/D rates, resulting in frequent misapplications and substantial losses in public resources. The objective of this study is to develop an effective method for determining I/D amounts for high-impact infrastructure projects. To achieve its objective, this study employs an integrated analysis to capture schedule, CAC, and total savings concurrently by combining an existing scheduling simulation with a stochastic analysis. A regression analysis is performed to predict the CAC growth rate by analyzing the relationship between CAC and the agency’s specific schedule goal. A stochastic model that accounts for heterogeneity of drivers’ value of time is developed to estimate the total savings achieved by early completion. The robustness of the proposed model is then validated through a case study.