Optimization of Selective Maintenance for Critical Infrastructures Considering Dynamic Environments

This study focuses on optimizing maintenance for critical infrastructures in dynamic environments. It proposes a modeling and solution method based on multi-stage stochastic processes and approximate dynamic programming. This study integrates environmental factors with the facility aging process to construct the network state transition mechanism. It also designs a multi-level maintenance decision-making system under resource constraints, aiming to balance maintenance costs and system reliability. The experimental findings indicate that the proposed method effectively improves maintenance strategies across networks of varying sizes. In short-term planning, the focus is on repairing components that have currently failed. In long-term planning, critical components with a high likelihood of future failure are proactively maintained. Environmental dynamics significantly impact the rate of equipment degradation, requiring flexible adjustments to maintenance strategies. This study highlights the benefits of the approximate dynamic programming algorithm in lowering computational complexity and effectively handling uncertainty, offering decision support for the long-term reliable operation of complex infrastructures.