Meta-Heuristic Solver with Parallel Genetic Algorithm Framework in Airline Crew Scheduling

Airline crew scheduling is a very important part of the operational planning commercial airlines, but it linear integer programming problem with multi-constraints. Traditionally, airline determined by solving pairing (CPP) and rostering (CRP), sequentially. In this paper, we propose new heuristic solver based on parallel genetic algorithm an innovative algorithm, which improves traditional integrating CPP CRP into single problem. The method includes global search adjustment for flights so as to realize scheduling. used divide population multiple threads calculation optimize randomly generated flight sequence maximize number that meet configuration. Compared CPLEX Gurobi, shows high optimization efficiency, time reduction 16.57–85.82%. experiment our utilization ratio higher than solvers, achieving almost 44 per month, good scalability stability in both 206 13,954 datasets, can better manage times scarcity.