Optimal data pooling for shared learning in maintenance operations

We study optimal data pooling for shared learning in two common maintenance operations: condition-based maintenance and spare parts management. We consider systems subject to Poisson input – the degradation or demand process – that are coupled through an unknown rate. Decision problems for these systems are high-dimensional Markov decision processes (MDPs) and are thus notoriously difficult to solve. We present a decomposition result that reduces such an MDP to two-dimensional MDPs, enabling structural analyses and computations. Leveraging this decomposition, we (i) show that pooling data can lead to significant cost reductions compared to not pooling, and (ii) prove that the optimal policy for the condition-based maintenance problem is a control limit policy, while for the spare parts management problem, it is an order-up-to level policy, both dependent on the pooled data.