01152384

Component

Currently there is a true dichotomy in the pavement asset maintenance and rehabilitation (M&R) literature. On the one hand, there are integer programming-based models that assume that parameters are deterministically known. On the other extreme, the authors have stochastic models that are based on the theory of Markov decision processes. In this paper wthe authors present an approach that is based on integer programming, but at the same time accounts for parameter uncertainties. Unlike virtually all existing stochastic models, the authors do not use the theory of Markov decision processes, i.e., othe authors model is non-Markovian. Another critical feature of the proposed models is that they provide probabilistic guarantees a priori that the prescribed M&R decisions would result in pavement condition scores that are above their critical service levels, using minimal assumptions regarding the sources uncertainty. By construction of the proposed models, the authors can easily determine the additional budget requirements when additional sources of uncertainty are considered, i.e., the price of uncertainty. A numerical case study presents valuable insights into the price of uncertainty and shows that the price of uncertainty can be large.

01147878

10-1179

English

15p

2010

Transportation Research Board 89th Annual Meeting

DVD

Figures (4) ; References; Tables (1)

Asset management; Budgeting; Decision making; Maintenance; Management; Markov processes; Pavement maintenance; Stochastic programming; Uncertainty

Administration and Management; Highways; Maintenance and Preservation; I10: Economics and Administration

Transportation Research Board Annual Meeting 2010 Paper #10-1179

TRIS, TRB

Jan 25 2010 10:31AM