Incorporating Economies of Scale in Top-Down Pavement Management Systems

Abstract

Top-down maintenance, rehabilitation, and reconstruction (MR&R) policies typically are modeled using Markov decision processes (MDPs). Within this framework, the total agency costs are assumed to increase linearly with the number of MR&R activities undertaken. However, there is empirical evidence to suggest that economies of scale (EoS) are present when agencies implement MR&R actions. EoS in infrastructure management refer to marginal savings in unit costs gained by increasing the scale of similar activities. This paper introduces EoS within a well-established system-level, top-down MR&R optimization framework for pavement management. In particular, given the concave nature of the proposed EoS-based objective function, two solution frameworks were implemented: a piecewise linear (PWL) approximation technique, and a branch-and-bound–based sigmoidal programming algorithm. Using a synthetic case study, the solution quality and computational efficiencies of the EoS-based problem formulations were compared with those of the fixed-unit-cost model. The resulting changes to the state–action distributions induced by the economies of scale are highlighted and discussed.