Approximate Dynamic Programming for Selective Maintenance in Series–Parallel Systems
Approximate Dynamic Programming for Selective Maintenance in Series–Parallel Systems
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DOI：
10.1109/tr.2019.2916898
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发表时间：
2020-09
影响因子：
5.9
通讯作者：
Khatereh Ahadi;K. M. Sullivan
中科院分区：
文献类型：
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作者：
Khatereh Ahadi;K. M. Sullivan
分配有限的资源来维护多部件系统的部件，称为选择性维护的问题，很自然地被制定为一个高维马尔可夫决策过程（MDP）。不幸的是，这些问题是很难解决的实际规模的系统。基于这个动机，我们提出了一个近似动态规划（ADP）算法来解决具有二状态部件的串并联系统的选择性维修问题。据我们所知，本文介绍了ADP的第一个应用程序，以维护多组分系统。我们的ADP进行了比较，使用文献中的数值例子，对相应的MDP的精确解。然后，我们总结了一组更全面的实验，证明ADP的良好性能在较大的情况下相比，精确的（但计算密集型）MDP方法和启发式（但计算速度更快）的一步前瞻性的方法。最后，我们证明了ADP是能够解决的扩展的基本选择性维护问题，在该问题中，允许跨阶段共享维护资源。
The problem of allocating limited resources to maintain components of a multicomponent system, known as selective maintenance, is naturally formulated as a high-dimensional Markov decision process (MDP). Unfortunately, these problems are difficult to solve exactly for realistically sized systems. With this motivation, we contribute an approximate dynamic programming (ADP) algorithm for solving the selective maintenance problem for a series–parallel system with binary-state components. To the best of our knowledge, this paper describes the first application of ADP to maintain multicomponent systems. Our ADP is compared, using a numerical example from the literature, against exact solutions to the corresponding MDP. We then summarize the results of a more comprehensive set of experiments that demonstrate the ADP's favorable performance on larger instances in comparison to both the exact (but computationally intensive) MDP approach and the heuristic (but computationally faster) one-step-lookahead approach. Finally, we demonstrate that the ADP is capable of solving an extension of the basic selective maintenance problem in which maintenance resources are permitted to be shared across stages.