  Humanitarian logistics service providers have two major responsibilities
immediately after a disaster: locating trapped people and routing aid to them.
These difficult operations are further hindered by failures in the
transportation and telecommunications networks, which are often rendered
unusable by the disaster at hand. In this work, we propose a two-echelon
vehicle routing framework for performing these operations using aerial uncrewed
autonomous vehicles (UAVs or drones) to address the issues associated with
these failures. In our proposed framework, we assume that ground vehicles
cannot reach the trapped population directly, but they can only transport
drones from a depot to some intermediate locations. The drones launched from
these locations serve to both identify demands for medical and other aids
(e.g., epi-pens, medical supplies, dry food, water) and make deliveries to
satisfy them. Specifically, we present a decision framework, in which the
resulting optimization problem is formulated as a two-echelon vehicle routing
problem with trucks as the first echelon vehicles and for the second echelon
vehicles, we consider two types of drones. Hotspot drones have the capability
of providing a cell phone and internet reception and hence are used to capture
demands. Delivery drones are subsequently employed to satisfy the observed
demand. To handle demand uncertainty, we decompose the decision problem into
two stages: providing telecommunications capabilities in the first stage
thereby capturing demand precisely, and satisfying the resulting demands in the
second stage. To solve the resulting models, we propose efficient computational
approaches by designing a decomposition algorithm with column generation
(CG)-based heuristics to identify optimal drone routes.
