Guo, C. and Liang, Z. (2022) A Predictive Markov Decision Process for Optimizing Inspection and Maintenance Strategies of Partially Observable Multi-State Systems. Reliability Engineering and System Safety, 226, Article ID: 108683.
https://doi.org/10.1016/j.ress.2022.108683

has been cited by the following article:

• TITLE: Analysis of a POMDP Model for an Optimal Maintenance Problem with Multiple Imperfect Repairs

  AUTHORS: Nobuyuki Tamura

  KEYWORDS: Partially Observable Markov Decision Process, Imperfect Repair, Stochastic Order, Monotone Property, Optimal Maintenance Policy

  JOURNAL NAME: American Journal of Operations Research, Vol.13 No.6, November 3, 2023

  ABSTRACT: I consider a system whose deterioration follows a discrete-time and discrete-state Markov chain with an absorbing state. When the system is put into practice, I may select operation (wait), imperfect repair, or replacement at each discrete-time point. The true state of the system is not known when it is operated. Instead, the system is monitored after operation and some incomplete information concerned with the deterioration is obtained for decision making. Since there are multiple imperfect repairs, I can select one option from them when the imperfect repair is preferable to operation and replacement. To express this situation, I propose a POMDP model and theoretically investigate the structure of an optimal maintenance policy minimizing a total expected discounted cost for an unbounded horizon. Then two stochastic orders are used for the analysis of our problem.