In this paper we present two adaptive control formulations that explicitly include uncertainty in characterizing a facility's deterioration rate in the process of developing maintenance and repair policies. The formulations use condition information that stems from the operation of a facility to improve the characterization over a finite planning horizon. We discuss issues related to the implementation and computational complexity of the formulations. Through a computational study, we show that the economic benefits can be achieved by implementing the adaptive control formulations. These benefits are most significant in situations where the initial expected deterioration rate of the facility is not a good representation of its actual deterioration rate.

Infrastructure management systems assist agencies in making decisions regarding maintenance, repair, and reconstruction of the facilities under their jurisdiction. The objective in these decision-making tools is to minimize the total expected cost of managing a system of facilities over a given planning horizon. Recent optimization models account for the uncertainty in the selection of facility performance models through an adaptive control approach.

In this paper, we extend the methodology to jointly determine when to inspect and what maintenance activity to perform, while taking into account uncertainty in measuring facility condition. A parametric study is performed to analyze the effect of the initial performance model uncertainty and bias on the expected total cost of managing a facility over a finite horizon. The parametric study shows that reducing model uncertainty leads, as expected, to lower costs. The results also indicate that reducing the initial variance in model uncertainty is more important than reducing the initial bias. In addition, our study shows that cost savings can result from relaxing the constraint of a fixed inspection schedule.