Adaptive optimization of infrastructure maintenance and inspection decisions under performance model uncertainty

We present an adaptive optimization model to find joint inspection and maintenance policies for infrastructure facilities under performance model uncertainty. The objective in the formulation is to minimize the total expected social cost of managing facilities over a finite 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 relax the assumptions of fixed and error-free inspections. We present a parametric study to analyze the effect of the initial performance model uncertainty and bias on the expected total cost of managing a facility. The main observation is that reducing the variance in model uncertainty leads to lower costs. Interestingly, the results also indicate that reducing the initial variance in model uncertainty can be more important than reducing the initial bias. Our study also shows that cost savings can result from relaxing the constraint of a fixed inspection schedule.