  Optimal decision-making is key to efficient allocation and scheduling of
repair resources (e.g., crews) to service affected nodes of large power grid
networks. Traditional manual restoration methods are inadequate for modern
smart grids sprawling across vast territories, compounded by the unpredictable
nature of damage and disruptions in power and transportation networks. This
paper develops a method that focuses on the restoration and repair efforts
within power systems. We expand upon the methodology proposed in the literature
and incorporate a real-world transportation network to enhance the realism and
practicality of repair schedules. Our approach carefully devises a reduced
network that combines vulnerable components from the distribution network with
the real transportation network. Key contributions include dynamically
addressing a coupled resource allocation and capacitated vehicle routing
problem over a new reduced network model, constructed by integrating the power
grid with the transportation network. This is performed using network
heuristics and graph theory to prioritize securing critical grid segments. A
case study is presented for the 8500 bus system.
