Interdependent integrated network design and scheduling problems with movement of machines

We consider the problem of restoring services provided by an interdependent set of infrastructures after they were disrupted from an extreme event. Specifically, we select the set of damaged infrastructure arcs for immediate restoration and schedule these on a set of machines (work crews). Our novel contribution is that when we determine the selection and scheduling of these damaged arcs, we explicitly consider the movement of machines through a damaged transportation network that is currently being restored. Previous works failed to consider how machine movement greatly influences the ability to conduct timely restoration due to the interdependence on the transportation network. To model this restoration construct, we propose an interdependent integrated network design and scheduling problem with movement of machines (IINDS-MM). In an IINDS-MM problem, we have a base transportation network and at least one additional infrastructure network layer. For each network layer, we determine what damaged arcs are selected for restoration, which machine will conduct the restoration, and the sequence of tasks assigned to each machine when explicitly considering machine movement through the changing transportation network. We propose a mixed integer programming formulation of the IINDS-MM problem and solve it using a rolling horizon solution procedure. Using realistic data representing Juan Diaz, Panama and the customizable artificial community CLARC data set, we simulate different storm surge levels and possible damage scenarios. We then solve the IINDS-MM problem and deduce insights about machine starting locations, machine capabilities, and the performance of IINDS-MM compared to existing restoration models.