Multi-Stage Stochastic Optimization and Reinforcement Learning Approaches for Dynamic Inspection of Infrastructure Systems
We study a dynamic inspection problem for infrastructure systems in which multiple vehicles are routed and scheduled to monitor components subject to heterogeneous and stochastic failures. A key challenge is endogenous uncertainty in which failure-time realizations are filtered through prior inspection actions and failure-propagation dynamics. This yields a multi-stage decision problem that integrates routing, scheduling, and decision-dependent reliability dynamics over time. We formulate the problem as a multi-stage stochastic mixed-integer program that jointly optimizes routing and scheduling decisions under endogenous uncertainty. We develop a stochastic dual dynamic integer programming (SDDiP) algorithm that integrates dual approximation, integer state reduction, and sampling-based forward simulation to approximate cost-to-go functions. In parallel, we propose a reinforcement learning framework that learns job clustering structures and routing policies through interaction with simulated system dynamics and failure processes. Numerical experiments on infrastructure networks with diverse topologies and failure patterns show that SDDiP yields high-quality solutions but faces scalability limitations, while the learning-based approach achieves strong scalability with competitive performance, highlighting a trade-off between optimality and tractability. This work advances the modeling of endogenous uncertainty in multi-stage stochastic routing problems and bridges stochastic programming with learning-based approaches. The results provide guidance on when to deploy optimization-based versus learning-based methods, enabling more effective and scalable inspection planning in practice. More broadly, the framework supports risk-aware allocation of inspection resources and improved reliability of critical infrastructure systems.