  Proper scheduling of air assets can be the difference between life and death
for a patient. While poor scheduling can be incredibly problematic during
hospital transfers, it can be potentially catastrophic in the case of a
disaster. These issues are amplified in the case of an air emergency medical
service (EMS) system where populations are dispersed, and resources are
limited. There are exact methodologies existing for scheduling missions,
although actual calculation times can be quite significant given a large enough
problem space. For this research, known coordinates of air and health
facilities were used in conjunction with a formulated integer linear
programming model. This was the programmed through Gurobi so that performance
could be compared against custom algorithmic solutions. Two methods were
developed, one based on neighbourhood search and the other on Tabu search.
While both were able to achieve results quite close to the Gurobi solution, the
Tabu search outperformed the former algorithm. Additionally, it was able to do
so in a greatly decreased time, with Gurobi actually being unable to resolve to
optimal in larger examples. Parallel variations were also developed with the
compute unified device architecture (CUDA), though did not improve the timing
given the smaller sample size.
