An integrated aircraft routing, crew scheduling and flight retiming model
Computers and Operations Research
Computers and Operations Research
Computers and Operations Research
Computers and Operations Research
Integrated Airline Fleet and Crew Robust Planning
Transportation Science
An iterative approach to robust and integrated aircraft routing and crew scheduling
Computers and Operations Research
Computers and Operations Research
General hybrid column generation algorithm for crew scheduling problems using genetic algorithm
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
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
Solving shortest path problems with a weight constraint and replenishment arcs
Computers and Operations Research
Integrated Airline Crew Pairing and Crew Assignment by Dynamic Constraint Aggregation
Transportation Science
Network design formulations for scheduling U.S. Air Force channel route missions
Mathematical and Computer Modelling: An International Journal
| Hi-index | 0.00 |
Crew costs are the second-largest operating expense faced by the airline industry, after fuel. Thus, even a small improvement in the quality of a crew schedule can have significant financial impact. Decisions made earlier in the airline planning process, however, can reduce the number of options available to the crew scheduler. We address this limitation by delaying some of these earlier planning decisions--specifically, key maintenance routing decisions--and incorporating them within the crew scheduling problem. We present anextended crew pairing model that integrates crew scheduling and maintenance routing decisions. We prove theoretical results that allow us to improve the tractability of this model by decreasing the number of variables needed and by relaxing the integrality requirement of many of the remaining variables. We discuss how to solve the model both heuristically and to optimality, providing the user with the flexibility to trade off solution time and quality. We present a computational proof-of-concept to support the tractability and effectiveness of our approach.