https://doi.org/10.1007/s43069-020-00016-1 ORIGINAL RESEARCH Dynamic Constraint Aggregation for Solving Very Large-scale Airline Crew Pairing Problems Guy Desaulniers 1,2 · Franc ¸ois Lessard 1,2 · Mohammed Saddoune 1,3 · Franc ¸ois Soumis 1,2 Received: 13 March 2020 / Accepted: 19 June 28020 / © Springer Nature Switzerland AG 2020 Abstract The monthly crew pairing problem (CPP) consists of determining a least-cost set of feasible crew pairings (sequences of flights starting and ending at a crew base) such that each flight is covered once and side constraints are satisfied. This problem has been widely studied but most works have tackled daily or weekly CPP instances with up to 3500 flights. Only a few papers have addressed monthly instances with up to 14,000 flights. In this paper, we propose an effective algorithm for solving very large-scale CPP instances. This algorithm combines, among others, column genera- tion (CG) with dynamic constraint aggregation (DCA) that can efficiently exploit the CG master problem degeneracy. When embedded in a rolling-horizon (RH) proce- dure, DCA allows to consider wider time windows in RH and yields better solutions. Our computational results show, first, the potential gains that can be obtained by using wider time windows and, second, the very good performance of the proposed algorithm when compared with a standard CG/RH algorithm for solving an industrial monthly CPP instance with 46,588 flights. Keywords Airline crew pairing problem · Large-scale instances · Column generation · Dynamic constraint aggregation This article belongs to the Topical Collection on: Decomposition at 70  Guy Desaulniers guy.desaulniers@gerad.ca 1 Department of Mathematics and Industrial Engineering, Polytechnique Montr´ eal, Montr´ eal, Canada 2 Group for Research in Decision Analysis (GERAD), Montr´ eal, Canada 3 Department of Computer Science, University of Hassan II, FST of Mohammedia, Casablanca, Morocco SN Operations Research Forum (2020) 1: 3 Published online: 1 2020 August 8