This paper presents the development of a pavement design and rehabilitation optimization decision-making framework based on Mechanistic-Empirical (ME) roughness transfer models. The AASHTOWare Pavement ME Design (the software of Pavement ME Design) is used to estimate pavement deterioration based on the combined effects of permanent deformation, fatigue, and thermal cracking. The optimization problem is first formulated into a mixed-integer nonlinear programming model to address the predominant trade-off between agency and user costs. To deal with the complexity associated with the pavement roughness transfer functions in the software and to use the roughness values as input to the optimization framework, a dynamic programming subroutine is developed for determining the optimal rehabilitation timing and asphalt concrete design thickness. An application of the proposed model is demonstrated in a case study. Managerial insights from a series of sensitivity analyses on different unit user cost values and model comparisons are presented
