• Corpus ID: 56042120

Application of a Maintenance Management Model for Iranian Railways Based on the Markov Chain and Probabilistic Dynamic Programming

@inproceedings{Shafahi2009ApplicationOA,
  title={Application of a Maintenance Management Model for Iranian Railways Based on the Markov Chain and Probabilistic Dynamic Programming},
  author={Yousef Shafahi and R. Hakhamaneshi},
  year={2009},
  url={https://api.semanticscholar.org/CorpusID:56042120}
}

• Y. Shafahi, R. Hakhamaneshi
• Published 2009
• Engineering

Railway managers have a strong economic incentive to minimize track maintenance costs, while maintaining safety standards and providing adequate service levels to train operators. The objective of this study is to apply a procedure for making optimal maintenance decisions in Iranian Railways. This study consists of two parts. First, a cumulative damage model, based on a Markov process, is applied to model the deterioration of the track. For this reason, tracks are categorized into six classes… 

Railway managers have a strong economic incentive to minimize track maintenance costs, while maintaining safety standards and providing adequate service levels to train operators. The objective of this study is to apply a procedure for making optimal maintenance decisions in Iranian Railways. This study consists of two parts. First, a cumulative damage model, based on a Markov process, is applied to model the deterioration of the track. For this reason, tracks are categorized into six classes, so that those tracks with similar tra c loads and geographical location are collected into one class. The track survey data from 215 blocks (4,228 km) of the ten divisions of the Iranian Railway system, during 2002-2004, is used to identify the transition matrix. Secondly, probabilistic dynamic programming is used to nd the optimal repair for each possible track state in the planning horizon. This approach allows an optimal maintenance decision to be determined for the track at any point in time within the planning horizon.