Application of non-Markovian stochastic Petri Nets to the modeling of rail system maintenance and availability

With the increasingly stringent contractual requirements placed on system availability in urban and intercity passenger rail systems, and the emergence of public-private partnerships with maintenance contracts over periods of 25 years or more, rail system suppliers such as ALSTOM Transport now adopt an integrated logistic support (ILS) vision, where the entire support system (maintenance policy, crew scheduling, spare parts, tools, etc.) is modeled at the same time as the main system. The need to overcome the restrictive assumptions imposed by Markov models has led us to the use of non-Markovian stochastic Petri Nets, which in addition lend themselves to building decentralized, hierarchical models. The challenges that are addressed in this paper are how to deal with: deferred maintenance, aging, and different time scales (not all units in the rail system have the same mission profile). Comparisons are made with results obtained with Markov models.