  Downtime of industrial assets such as wind turbines and medical imaging
devices is costly. To avoid such downtime costs, companies seek to initiate
maintenance just before failure, which is challenging because: (i) Asset
failures are notoriously difficult to predict, even in the presence of
real-time monitoring devices which signal degradation; and (ii) Limited
resources are available to serve a network of geographically dispersed assets.
In this work, we study the dynamic traveling multi-maintainer problem with
alerts ($K$-DTMPA) under perfect condition information with the objective to
devise scalable solution approaches to maintain large networks with $K$
maintenance engineers. Since such large-scale $K$-DTMPA instances are
computationally intractable, we propose an iterative deep reinforcement
learning (DRL) algorithm optimizing long-term discounted maintenance costs. The
efficiency of the DRL approach is vastly improved by a reformulation of the
action space (which relies on the Markov structure of the underlying problem)
and by choosing a smart, suitable initial solution. The initial solution is
created by extending existing heuristics with a dispatching mechanism. These
extensions further serve as compelling benchmarks for tailored instances. We
demonstrate through extensive numerical experiments that DRL can solve single
maintainer instances up to optimality, regardless of the chosen initial
solution. Experiments with hospital networks containing up to $35$ assets show
that the proposed DRL algorithm is scalable. Lastly, the trained policies are
shown to be robust against network modifications such as removing an asset or
an engineer or yield a suitable initial solution for the DRL approach.
