Restorative Capacity Optimization for Complex Infrastructure Networks

This paper focuses on the planning and scheduling of restoration efforts provided by infrastructure networks in the aftermath of disruptive events. Two mathematical formulations are presented to assign restoration crews to disrupted components and maximize network resilience progress in any given time horizon. In the first formulation, the number of assigned restoration crews to each component can vary to increase the flexibility of models in the presence of different disruption scenarios. Along with considering the assumptions of the first formulation, the second formulation models the condition where the disrupted components can be partially active during the restoration process. We test the efficacy of our formulations on the realistic dataset of 400-kV French electric transmission network and 32 realistic size datasets illustrating scale-free, small-world, lattice, and random networks. The results indicate that the proposed formulations can be used for a wide variety of infrastructure networks and for real-time restoration process planning.