Maintenance optimisation of a parallel-series system with stochastic and economic dependence under limited maintenance capacity

Maintenance optimisation of a parallel-series system considering both stochastic and economic dependence among components as well as limited maintenance capacity is studied in this paper. The maintenance strategies of the components are jointly optimised, and the degradation process of the system is modelled to address the stochastic dependence and limited maintenance capacity issues. To overcome the “curse of dimensionality” problem where the state space of a parallel-series system increases rapidly with the increased number of components in the system, the factored Markov decision process (FMDP) is employed for maintenance optimisation in this work. An improved approximate linear programming (ALP) algorithm is then developed. The selection of the basis functions and the state relevance weights for ALP is also investigated to enhance the performance of the ALP algorithm. Results from the numerical study show that the current approach can handle the decision optimisation problem for multi-component systems of moderate size, and the error of maintenance decision-making induced by the improved ALP is negligible. The outcome from this research provides a useful reference to overcome the “curse of dimensionality” problem during the maintenance optimisation of multi-component systems.