A POMDP framework for integrated scheduling of infrastructure maintenance and inspection

Abstract

This work presents an optimization scheme for maintenance and inspection scheduling of the infrastructure system whose states are nearly impossible or prohibitively expensive to estimate or measure online. The suggested framework describes state transition under the observation uncertainty as Partially Observable Markov Decision Process (POMDP) and can integrate heterogeneous scheduling jobs including maintenance, inspection, and sensor installation within a single model. The proposed approach performs survival analysis to obtain time-variant transition probabilities. A POMDP problem is then formulated via state augmentation. The resulting large-scale POMDP is solved by an approximate point-based solver. We exploit the idea of receding horizon control to the POMDP framework as a feedback rule for the online evaluation. Water distribution pipeline is analyzed as an illustrative example, and the results indicate that the proposed POMDP framework can improve the overall cost for maintenance tasks and thus the system’s sustainability.