带警报的动态旅行多维护者问题的可扩展策略
Scalable Policies for the Dynamic Traveling Multi-Maintainer Problem with Alerts
AI总结：
针对工业资产维护的带警报动态旅行多维护者问题（$K$-DTMPA），提出迭代深度强化学习算法，通过重构动作空间和智能初始解优化长期成本，实验证明其能求解至最优、具可扩展性且对网络修改具鲁棒性。
摘要：
风力涡轮机和医疗成像设备等工业资产的停机代价高昂。为避免此类停机成本，企业力求在故障发生前刚好启动维护，这极具挑战性，原因在于：(i) 即使存在发出退化信号的实时监控设备，资产故障也极难预测；以及 (ii) 可用于服务地理上分散资产网络的资源有限。在本研究中，我们在完美状态信息下研究带警报的动态旅行多维护者问题（$K$-DTMPA），旨在设计可扩展的求解方法，以维护配备 $K$ 名维护工程师的大型网络。由于此类大规模 $K$-DTMPA 实例在计算上难以处理，我们提出了一种迭代深度强化学习（DRL）算法，以优化长期折扣维护成本。通过对动作空间进行重构（这依赖于底层问题的马尔可夫结构）以及选择智能且合适的初始解，DRL方法的效率得到了极大提升。初始解通过使用调度机制扩展现有启发式方法来创建。这些扩展进一步作为定制实例的有力基准。我们通过广泛的数值实验证明，无论选择何种初始解，DRL都能将单维护者实例求解至最优。包含多达 $35$ 个资产的医院网络实验表明，所提出的DRL算法具有可扩展性。最后，训练出的策略被证明对网络修改（如移除资产或工程师）具有鲁棒性，或者能为DRL方法提供合适的初始解。
英文摘要：
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.
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