Optimal data pooling for shared learning in maintenance operations

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Optimal data pooling for shared learning in maintenance operations

Authors

Collin Drent, Melvin Drent, Geert-Jan van Houtum

Abstract

This paper addresses the benefits of pooling data for shared learning in maintenance operations. We consider a set of systems subject to Poisson degradation that are coupled through an a-priori unknown rate. Decision problems involving these systems are high-dimensional Markov decision processes (MDPs). We present a decomposition result that reduces such an MDP to two-dimensional MDPs, enabling structural analyses and computations. We leverage this decomposition to demonstrate that pooling data can lead to significant cost reductions compared to not pooling.

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