  Downtime of industrial assets such as wind turbines and medical imaging
devices comes at a sharp cost. To avoid such downtime costs, companies seek to
initiate maintenance just before failure. Unfortunately, this is challenging
for the following two reasons: On the one hand, because asset failures are
notoriously difficult to predict, even in the presence of real-time monitoring
devices which signal early degradation. On the other hand, because the
available resources to serve a network of geographically dispersed assets are
typically limited. In this paper, we propose a novel dynamic traveling
maintainer problem with alerts model that incorporates these two challenges and
we provide three solution approaches on how to dispatch the limited resources.
Namely, we propose: (i) Greedy heuristic approaches that rank assets on
urgency, proximity and economic risk; (ii) A novel traveling maintainer
heuristic approach that optimizes short-term costs; and (iii) A deep
reinforcement learning (DRL) approach that optimizes long-term costs. Each
approach has different requirements concerning the available alert information.
Experiments with small asset networks show that all methods can approximate the
optimal policy when given access to complete condition information. For larger
networks, the proposed methods yield competitive policies, with DRL
consistently achieving the lowest costs.
