Selective Maintenance Optimization under Uncertainty: Stochastic Programming Approach

This research delves into the idea of improving the likelihood of mission success in multi-component systems through selective maintenance during downtime. It considers uncertainties surrounding future mission conditions. Stochastic programming is utilized to represent these uncertainties accurately. The study presents two distinct models: one focusing on uncertainty in operational conditions, while the other expands to include uncertainties in operating time. The efficacy of the proposed model underscores the importance of integrating uncertainty considerations into the broader model, enhancing our understanding of the system's performance robustness.