Constraint Programming Based Column Generation for Crew Assignment
Journal of Heuristics
Multiobjective genetic algorithm to solve the train crew scheduling problem
ISTASC'10 Proceedings of the 10th WSEAS international conference on Systems theory and scientific computation
On a New Rotation Tour Network Model for Aircraft Maintenance Routing Problem
Transportation Science
On a New Rotation Tour Network Model for Aircraft Maintenance Routing Problem
Transportation Science
Exact approaches for integrated aircraft fleeting and routing at TunisAir
Computational Optimization and Applications
Integrated Airline Crew Pairing and Crew Assignment by Dynamic Constraint Aggregation
Transportation Science
Aircrew pairings with possible repetitions of the same flight number
Computers and Operations Research
A hybrid meta-heuristic algorithm for optimization of crew scheduling
Applied Soft Computing
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The crew pairing problem requires the coverage of a set of long-haul flights by a minimum cost set of crew pairings. A crew pairing is a sequence of flights flown by one crew, starting and ending at the same location, and satisfying a variety of work regulations and collective bargaining agreements. We present a new solution approach that solves first an approximate model of the problem and then uses its solution as an advanced start solution for conventional approaches. Using data provided by a long-haul airline, we demonstrate that our new approach can be used with a deadhead selector to identify deadheads quickly that might improve significantly the quality of the crew pairing solution. Deadheads, flights to which crews are assigned as passengers, reposition crews for better utilization. We speed up the solution process by using our advanced start solution and by quickly providing good lower bounds on the optimal solution values. Our experiments show that the lower bounds are on average within 0.85% of the optimal solution value. Further, we show that compared to existing methods, we reduce solution costs and run times by an average of 20% and over 80%, respectively.