Abstract–

We study the problem of inspection and maintenance planning of capital goods based on observations of the capital good’s degradation state. However, the observations are imprecise, and their quality depends on the environment. For example, when performing maintenance for heating, ventilation, and air-conditioning units (HVACs) in trains, the health of the cooling component of an HVAC can be assessed from temperature readouts of the car in which the HVAC is mounted. Temperature information is useful in the summer when high car temperatures can indicate a failed cooling component, but this information has limited value during the winter. We model the problem as a partially observable Markov decision process with a fully observed environment. We analytically show that an environment-dependent monotonic at-most-4-region policy is optimal. Furthermore, we numerically analyze an example motivated by HVAC maintenance at Dutch Railways. This analysis shows that, in many cases, including the environment in the model can lead to cost savings of more than 10%. In a broad numerical experiment, we show that a simple policy cannot always substitute an optimal policy.

摘要-

我们根据对资本货物退化状态的观察来研究资本货物的检查和维护计划问题。然而，观察并不精确，其质量取决于环境。例如，在对火车中的供暖、通风和空调装置 (HVAC) 进行维护时，可以根据安装 HVAC 的车厢的温度读数来评估 HVAC 冷却组件的运行状况。温度信息在夏季很有用，因为汽车温度高可能表明冷却部件出现故障，但该信息在冬季价值有限。我们将问题建模为具有完全可观察环境的部分可观察马尔可夫决策过程。我们分析表明，依赖于环境的单调至多 4 个区域策略是最优的。此外，我们对荷兰铁路 HVAC 维护引发的示例进行了数值分析。此分析表明，在许多情况下，将环境纳入模型中可以节省 10% 以上的成本。在广泛的数值实验中，我们表明简单的策略并不总是能替代最优策略。