Algorithms for Optimizing Fleet Staging of Air Ambulances
  In a disaster situation, air ambulance rapid response will often be the
determining factor in patient survival. Obstacles intensify this circumstance,
with geographical remoteness and limitations in vehicle placement making it an
arduous task. Considering these elements, the arrangement of responders is a
critical decision of the utmost importance. Utilizing real mission data, this
research structured an optimal coverage problem with integer linear
programming. For accurate comparison, the Gurobi optimizer was programmed with
the developed model and timed for performance. A solution implementing base
ranking followed by both local and Tabu search-based algorithms was created.
The local search algorithm proved insufficient for maximizing coverage, while
the Tabu search achieved near-optimal results. In the latter case, the total
vehicle travel distance was minimized and the runtime significantly
outperformed the one generated by Gurobi. Furthermore, variations utilizing
parallel CUDA processing further decreased the algorithmic runtime. These
proved superior as the number of test missions increased, while also
maintaining the same minimized distance.
