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.