Abstract Traditionally, the initial steps on airline planning – Schedule Generation and Fleet Assignment problems – are solved separately. This traditional approach usually leads to suboptimal solutions, since flight profitability – the decision criteria to schedule a flight – depends on what aircraft type will be used on that flight. On the other hand, the type of aircraft assigned to a flight will be different accordingly to the available scheduled flights. Because of this interdependence, airlines avoid complete redesign of their flight network, adopting a conservative approach and slightly improving existing suboptimal schedules over time. The integrated solution for both problems, albeit desirable, leads to large-scale models of the NP-Hard class. Some of the original linear constraints may become non-linear in the integrated problem, bringing further complexity to the solution process. This article presents a linear programming formulation of this integrated problem along with a heuristic approach, called MAGS, based on the ACO metaheuristic. Both the exact solution and the one provided by MAGS are obtained and compared for the case of a Brazilian airline. The results show the applicability of MAGS to real world cases, presenting solutions with objective function values distant no more than 6% of the optimum and much lower processing times than LP model on more complex configurations.

A typical problem related to airline crew management consists of optimally assigning the required crew members to planned flights for a given period of time, while complying with a variety of labor regulations, safety rules and policies of the airline. This problem, called crew assignment problem (CAP), is of the NPHard class. So, it is usually divided into two independent subproblems, crew pairing problem (CPP) and crew rostering problem (CRP), modeled and solved sequentially. This division does not provide a global treatment to the CAP in terms of total cost and quality of the final solution. The state of the art involves the integrated solution of CAP, with both subproblems (CPP and CRP) solved simultaneously. It still requires high computational effort. Its combinatorial nature makes it difficult (or even impossible) to be solved by exact methods. The methodology proposed in this research provides an integrated solution of the CAP with heuristic procedures. The methodology was tested to solve instances related to small and medium-sized Brazilian airlines. The results were also compared with those obtained through an exact model adapted from the literature.