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 (-DTMPA) under perfect condition information with the objective to devise scalable solution approaches to maintain large networks with maintenance engineers. Since such large-scale -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.

中文翻译：

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针对带有警报的动态移动多维护者问题的可扩展策略


风力涡轮机和医疗成像设备等工业资产的停机成本高昂。为了避免此类停机成本，公司寻求在发生故障之前启动维护，这是一项具有挑战性的工作，因为： (i) 众所周知，资产故障很难预测，即使存在发出退化信号的实时监控设备； (ii) 可用于服务地理分散的资产网络的资源有限。在这项工作中，我们研究了完美条件信息下的动态移动多维护者警报问题（-DTMPA），目的是设计可扩展的解决方案来与维护工程师一起维护大型网络。由于这种大规模的-DTMPA实例在计算上很难处理，我们提出了一种迭代深度强化学习（DRL）算法来优化长期折扣维护成本。通过重新制定动作空间（依赖于潜在问题的马尔可夫结构）和选择智能、合适的初始解决方案，DRL 方法的效率得到了极大的提高。最初的解决方案是通过使用调度机制扩展现有的启发式方法来创建的。这些扩展进一步充当定制实例的令人信服的基准。我们通过大量的数值实验证明，无论选择何种初始解决方案，DRL 都可以解决单个维护者实例的最优问题。对包含多达 35 个资产的医院网络进行的实验表明，所提出的 DRL 算法具有可扩展性。最后，经过训练的策略对于网络修改（例如删除资产或工程师）具有鲁棒性，或者为 DRL 方法提供合适的初始解决方案。