A stochastic dynamic programming for maintenance planning of an emergency helicopter

Abstract

Emergency/rescue helicopters are an essential part of the healthcare systems. Every helicopter should be kept in its best possible operational mode to save the life of the people in danger. For achieving this goal, using an optimal maintenance planning is crucial, which involves a periodic decision about repair or replacement of the components in the helicopter. But, in the considered case study, the spare part inventory is limited and cannot be easily replenished. Also, an observation in this case study is that a repaired component may have a shorter probabilistic lifetime (i.e. time to next failure) in contrast to a brand-new one. The best sequence of decisions can be made for each day of a given planning horizon, when different probabilistic trade-offs for every mission hour are taken into account. The problem is formulated in the framework of a stochastic dynamic program (SDP). For obtaining a fast near-optimal policy, an approximate dynamic program (ADP) is developed, which is based on the Monte-Carlo sampling of the possible paths in the corresponding transition graph of the SDP. A numerical experiment shows its gradual convergence toward an optimal policy as it spends more time in the search space of the SDP.

Keywords:

• Stochastic dynamic program (SDP)
• approximate dynamic program (ADP)
• maintenance
• spare part inventory
• emergency helicopter

Acknowledgments

Authors are thankful to several people for their support in publishing this research paper, especially the editor-in-chief, three reviewers and associate editor.

Data availability statement

The authors confirm that the data supporting the findings of this study are available within the article [and/or] its supplementary materials.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Mehdi Karimi-Nasab

Mehdi Karimi-Nasab finished his PhD in 2013 in Iran University of Science and Technology, with a specialization in ‘algorithmic optimization’ by publishing in journals such as European Journal of Operational Research, International Journal of Production Research, and Computers & Industrial Engineering. Then, he moved to the University of Hamburg in 2015, where he was awarded a two-year Georg-Forster research fellowship from the Alexander von Humboldt foundation. There, he started a long journey on learning and specializing on ‘SDP’ and related issues such as ‘curse of dimensionality’, ‘analytical solutions’, ‘ADP’, and ‘parallelization of computations.’ In 2021, he worked at the Technical University of Munich, and researched on the combination of ‘SDP’ and ‘game theory’.

Kamyar Sabri-Laghaie

Kamyar Sabri-Laghaie received the BS degree in industrial engineering from the Khajeh Nasir Toosi University of Technology in 2008, and the MS and PhD degrees in industrial engineering from the Iran University of Science and Technology in 2011 and 2015, respectively. Since 2017, he is working at the Urmia University of Technology as an assistant professor, where he focuses on researching about maintenance/reliability optimization, with specialization/application in civil aviation. Further, he is interested in those applications, which involve using data analytics. He has a publication record in IEEE Transactions on Reliability, Reliability Engineering & System Safety, International Journal of Production Research, and Computers & Industrial Engineering.