Hybrid Simulation and Reinforcement Learning-Based Scheduling for Resilient Infrastructure Networks

Infrastructure systems are interdependent at various levels, and their collective performance is influenced by factors such as topology, budgetary decisions, resource availability, and awareness of interdependency. Traditional resource allocation models for improving resilience often assume a single decision-maker overseeing all scheduling decisions. However, critical infrastructures, characterized by a network-of-networks structure, are managed by individual entities with distinct boundaries. Moreover, the dynamic and stochastic nature of decision-making processes cannot always be captured via mathematical programming. This study develops a hybrid simulation model that merges top-down and bottom-up approaches. It captures organizational-level budgetary decision-making dynamics through system dynamics, and maintenance activities alongside evolving network performance through an agent-based model. Optimal restoration strategies maximizing network resilience are identified via deep reinforcement learning, constrained by financial allocations. This approach is applied to water distribution and mobility networks in Tampa, FL demonstrating our method's efficacy for restoring interdependent infrastructures.