5 research outputs found
Real-World Airline Crew Pairing Optimization: Customized Genetic Algorithm versus Column Generation Method
Airline crew cost is the second-largest operating cost component and its
marginal improvement may translate to millions of dollars annually. Further,
it's highly constrained-combinatorial nature brings-in high impact research and
commercial value. The airline crew pairing optimization problem (CPOP) is aimed
at generating a set of crew pairings, covering all flights from its timetable,
with minimum cost, while satisfying multiple legality constraints laid by
federations, etc. Depending upon CPOP's scale, several Genetic Algorithm and
Column Generation based approaches have been proposed in the literature.
However, these approaches have been validated either on small-scale flight
datasets (a handful of pairings) or for smaller airlines (operating-in
low-demand regions) such as Turkish Airlines, etc. Their search-efficiency gets
impaired drastically when scaled to the networks of bigger airlines. The
contributions of this paper relate to the proposition of a customized genetic
algorithm, with improved initialization and genetic operators, developed by
exploiting the domain-knowledge; and its comparison with a column generation
based large-scale optimizer (developed by authors). To demonstrate the utility
of the above-cited contributions, a real-world test-case (839 flights),
provided by GE Aviation, is used which has been extracted from the networks of
larger airlines (operating up to 33000 monthly flights in the US).Comment: 7 pages, 3 figure