Modern communities heavily depend on critical infrastructure networks such as power, water, transportation, telecommunications, gas, etc. Since daily life requires these networks to be operational, it is important that they are able to withstand or recover quickly from a disruption, a term known as resilience. These infrastructure networks are often dependent on each other for operation. The interdependency of infrastructure networks makes them more vulnerable to disruptive events such as malevolent attacks, natural disasters, and random failures. The operability of these networks may be compromised following a disruptive event such that demand in any given network is not met. To return the networks to some desired level of resilience, work crews must be scheduled to restore certain disrupted elements. The proposed model is a multi-objective mixed-integer programming model that seeks to minimize the total cost of restoration while maximizing the combined resilience of interdependent infrastructure networks. The model may be used to determine where each work crew should originate from following a disruptive event as well as schedule the work crews to restore disrupted network elements over a finite time horizon. This work demonstrates the use of the model through an illustrative example of two interdependent infrastructure networks. Considering four disruption scenarios, this illustrative example shows how recovery may change by varying the number of facilities established for work crews in each network
