Real-World Airline Crew Pairing Optimization: Customized Genetic Algorithm versus Column Generation Method
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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).
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