An Improved Fast Search Multi-objective Genetic Algorithm for Airline Crew Scheduling Problems

Most of the existing studies about airline crew scheduling problems focus on single-objective optimization or multi-objective optimization under simple constraints. In this paper, we propose an airline crew scheduling model based on a large number of constraints in actual scenarios, with multiple objectives for both saving airline company’s cost and improving the balance of crew working time. We then introduce an improved fast search multi-objective genetic algorithm, which is able to quickly find the feasible domain around optimal solution when confronting with a large number of crew data and constraints. We compare the results of our algorithm with the multi-objective greedy algorithm, which verifies the effectiveness of our algorithm on solving the airline crew scheduling problem.