Abstract
Airline crew scheduling is a very visible and economically significant problem faced by airline industry. Set partitioning problem (SPP) is a role model to represent & solve airline crew scheduling problem. SPP itself is highly constrained combinatorial optimization problem so no algorithm solves it in polynomial time. In this paper we present a genetic algorithm (GA) using new Cost-based Uniform Crossover (CUC) for solving set partitioning problem efficiently. CUC uses cost of the column information for generating offspring. Performance of GA using CUC is evaluated using 28 real-world airline crew scheduling problems and results are compared with well-known IP optimal solutions & Levine’s GA solutions [13].
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