Improving Air Crew Rostering by Considering Crew Preferences in the Crew Pairing Problem

A common strategy used by airlines to improve employee satisfaction is to create schedules that take into account crew preferences such as preferred legs or desired off-periods. Air crew scheduling usually involves two steps: the crew pairing problem (CPP) and the crew rostering problem (CRP). A pairing is a sequence of legs and deadheads separated by connections and rest periods that starts and ends at the same crew base and can legally be operated by a crew member. The CPP generates a set of pairings that covers every leg of a given schedule exactly once at a minimum cost. The CRP uses these pairings to create rosters composed of personalized schedules, with the goal of granting as many crew preferences as possible. A downside of this two-step approach is that the CPP does not take the crew preferences into account, resulting in CPP solutions that are often ill suited for the CRP. In this paper, we propose a new variant of the CPP, called the CPP with complex features (CPPCF), that considers the crew preferences in order to create pairings that are better suited for the CRP. Specifically, we identify six pairing features related to crew preferences that are beneficial for the CRP, and the objective function of the CPPCF rewards pairings that contain these features. We solve the CPPCF using a column generation algorithm in which new pairings are generated by solving subproblems consisting of constrained shortest path problems. For this purpose, we introduce a new type of path resources designed to handle complex features, and we adapt the dominance rules accordingly. We test the proposed CPPCF approach on seven real-world instances from a major North American airline and show that a combination of these features significantly improves the solutions of the CRP.