An Improved Honey Badger Algorithm by Genetic Algorithm and Levy Flight Distribution for Solving Airline Crew Rostering Problem

Abstract

Airline crew rostering problem contains a variety of rules and constraints, and there are almost countless possible scheduling schemes. It is the most complex and important link in the entire crew scheduling plan. In this paper, we build a model that includes qualification constraints. In this paper, we consider two models with qualification constraints with different objective functions, namely minimizing the total cost of the airline and balancing flight utility among pilots as much as possible. To solve this model, the Levy flight is used to improve the ability of the Honey Badger Algorithm (HBA) to jump out of local optima, and the crossover and mutation operators in the Genetic Algorithm (GA) are used to improve the quality of the solution. This improved HBA algorithm significantly improves convergence and solution accuracy. In addition to this, we verified the improved HBA algorithm on 6 instances, of which 4 instances do not contain any qualifications, and 2 instances contain high-qualification flight pairings. The good results of the improved HBA show that it has excellent performance in both objective functions.