Solving Large Airline Crew Scheduling Problems: Random Pairing Generation and Strong Branching
Computational Optimization and Applications
Branch-And-Price: Column Generation for Solving Huge Integer Programs
Operations Research
An Approximate Model and Solution Approach for the Long-Haul Crew Pairing Problem
Transportation Science
Benders Decomposition for Simultaneous Aircraft Routing and Crew Scheduling
Transportation Science
Airline Crew Scheduling with Time Windows and Plane-Count Constraints
Transportation Science
Improving Crew Scheduling by Incorporating Key Maintenance Routing Decisions
Operations Research
Computers and Operations Research
Dynamic Aggregation of Set-Partitioning Constraints in Column Generation
Operations Research
Robust Airline Crew Pairing: Move-up Crews
Transportation Science
Bi-dynamic constraint aggregation and subproblem reduction
Computers and Operations Research
Integrated Airline Fleeting and Crew-Pairing Decisions
Operations Research
Integrated Airline Fleet and Crew Robust Planning
Transportation Science
Multi-phase dynamic constraint aggregation for set partitioning type problems
Mathematical Programming: Series A and B
Aircrew pairings with possible repetitions of the same flight number
Computers and Operations Research
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Traditionally, the airline crew scheduling problem has been decomposed into a crew pairing problem and a crew assignment problem, both of which are solved sequentially. The first consists of generating a set of least-cost crew pairings (sequences of flights starting and ending at the same crew base) that cover all flights. The second aims at finding monthly schedules (sequences of pairings) for crew members that cover all pairings previously built. Pairing and schedule construction must respect all safety and collective agreement rules. In this paper, we focus on the pilot crew scheduling problem in a bidline context where anonymous schedules must be built for pilots and high fixed costs are considered to minimize the number of scheduled pilots. We propose a model that completely integrates the crew pairing and crew assignment problems, and we develop a combined column generation/dynamic constraint aggregation method for solving them. Computational results on real-life data show that integrating crew pairing and crew assignment can yield significant savings---on average, 3.37% on the total cost and 5.54% on the number of schedules for the 7 tested instances. The integrated approach, however, requires much higher computational times than the sequential approach.