This work presents an optimization scheme for maintenance and inspection scheduling of the infrastructure system whose states are nearly impossible or prohibitively expensive to estimate or measure online. The suggested framework describes state transition under the observation uncertainty as Partially Observable Markov Decision Process (POMDP) and can integrate heterogeneous scheduling jobs including maintenance, inspection, and sensor installation within a single model. The proposed approach performs survival analysis to obtain time-variant transition probabilities. A POMDP problem is then formulated via state augmentation. The resulting large-scale POMDP is solved by an approximate point-based solver. We exploit the idea of receding horizon control to the POMDP framework as a feedback rule for the online evaluation. Water distribution pipeline is analyzed as an illustrative example, and the results indicate that the proposed POMDP framework can improve the overall cost for maintenance tasks and thus the system’s sustainability.

这项工作为基础设施系统的维护和检查计划提供了一种优化方案，该基础设施系统的状态几乎不可能在线估计或测量，或者其成本很高。建议的框架将观测不确定性下的状态转换描述为部分可观测的马尔可夫决策过程（POMDP），并且可以将包括维护，检查和传感器安装在内的异构调度工作整合到一个模型中。所提出的方法执行生存分析以获得时变过渡概率。然后通过状态扩充来表达POMDP问题。通过近似的基于点的求解器求解生成的大规模POMDP。我们利用将地平线控制后退到POMDP框架的想法作为在线评估的反馈规则。