Optimal data pooling for shared learning in maintenance operations
This work addresses data pooling challenges for maintenance operations, offering incremental improvements in policy design and computational efficiency.
The paper tackles the problem of optimal data pooling for shared learning in maintenance operations, specifically condition-based maintenance and spare parts management, by presenting a decomposition result that reduces high-dimensional Markov decision processes to two-dimensional ones, leading to significant cost reductions and identifying optimal policy structures.
We study optimal data pooling for shared learning in two common maintenance operations: condition-based maintenance and spare parts management. We consider a set of systems subject to Poisson input -- the degradation or demand process -- that are coupled through an a-priori unknown rate. Decision problems involving these systems are high-dimensional Markov decision processes (MDPs) and hence 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) demonstrate that pooling data can lead to significant cost reductions compared to not pooling, and (ii) show 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.