A Multiobjective Evolutionary Solution for Short-Haul Airline Crew Pairing Problem

Abstract

The Airline Crew Pairing Problem (ACPP) which consists of finding crew itineraries and satisfying the related law and regulation constraints is a significantly economic challenge. And many efforts have been spent by airline industry in the search for efficient and effective solutions. Instead of using the traditional set partitioning model, a different view is adopted here to model the crewing problem and formulate it with a set of combinational optimization equations. In general, there are two phases in crew pairing, such as pairing generation and pairing optimization to be solved. A method of inequality-based multiobjective genetic algorithm (MMGA) is used here to provide the solution and solve them at the same time. Besides, with the Method of Inequalities (MOI), designers can configure the ranges of solutions by adjusting an auxiliary vector of performance indices. In practice, the proposed MMGA approach possesses the merits of global exploration and can provide several optimal or feasible solutions to help planners perform efficient and effective decision-making.