Skip to main content

An optimal policy for partially observable Markov decision processes with non-independent monitors

Buy Article:

$37.12 plus tax (Refund Policy)

Purpose - This research investigated the optimal structure of a discrete-time Markov deterioration system monitored by multiple non-independent monitors. The purpose is to obtain a sufficient condition with which the optimal policy is given by a control limit policy. Design/methodology/approach - The model of this research is formulated as a partially observable Markov decision process. The problem is to obtain an optimal policy which can minimize the expected total discounted cost over an infinite horizon. Findings - The research found that the expected optimal cost function over an infinite horizon has a property of control limit policy given the conditions that a transition probability having a property of totally positive of order 2 and a conditional probability of the monitors having a property of weak multivariate monotone likelihood ratio. Furthermore, we showed that the optimal policy has only four action regions at most. Practical implications - If the optimum policy can be limited to a control limit policy, the tremendous amount of calculation time required to find the optimum procedure can be reduced. This enables the best decision to be identified in a much shorter period of time. Originality/value - A deterioration system monitored incompletely by one monitor has been studied in the previous research. This research considered the case of a multiple number monitors whose observations were not independent.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
No Metrics

Keywords: Condition Monitoring; Failure (Mechanical); Optimization Techniques; Reliability Management

Document Type: Research Article

Publication date: 2005-03-01

  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more