Policies for the Dynamic Traveling Maintainer Problem with Alerts

TL;DR

This paper introduces a new dynamic maintenance scheduling problem considering alerts and limited resources, proposing three solution methods including heuristics and deep reinforcement learning, with experiments showing competitive performance especially for DRL.

Contribution

It formulates the dynamic traveling maintainer problem with alerts and develops three innovative solution approaches, including a novel DRL method.

Findings

All methods approximate optimal policies with complete information.

Proposed methods perform well on larger networks.

DRL achieves the lowest costs among the approaches.

Abstract

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.