Maintenance and repair decision making for infrastructure facilities without a deterioration model

In the existing approach to maintenance and repair decision making for infrastructure facilities, policy evaluation and policy selection are performed under the assumption that a perfect facility deterioration model is available. The writer formulates the problem of developing maintenance and repair policies as a reinforcement learning problem in order to address this limitation. The writer explains the agency-facility interaction considered in reinforcement learning and discuss the probing-optimizing dichotomy that exists in the process of performing policy evaluation and policy selection. Then, temporal-difference learning methods are described as an approach that can be used to address maintenance and repair decision making. Finally, the results of a simulation study are presented where it is shown that the proposed approach can be used for decision making in situations where complete and correct deterioration models are not (yet) available.