Optimization of inspection and maintenance decisions for infrastructure facilities under performance model uncertainty: A quasi-Bayes approach

We present an 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. As in recent optimization models, performance model uncertainty is accounted for by representing facility deterioration as a mixture of known models taken from a finite set. The mixture proportions are assumed to be continuous random variables, with probability densities that are updated over time. In this paper, we relax the assumptions of fixed and error-free inspections. We present a parametric study to analyze the effect of initial performance model uncertainty and bias on the expected total cost of managing a facility. The main observation is that reducing the initial variance in model uncertainty may 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.