The Operational Airline Crew Scheduling Problem
Transportation Science
A Stochastic Model of Airline Operations
Transportation Science
A Robust Fleet-Assignment Model with Hub Isolation and Short Cycles
Transportation Science
Operational airline reserve crew planning
Journal of Scheduling
Robust crew pairing for managing extra flights
Computers and Operations Research
Integrated Airline Fleet and Crew Robust Planning
Transportation Science
Disruption management in the airline industry-Concepts, models and methods
Computers and Operations Research
A multi-objective approach for robust airline scheduling
Computers and Operations Research
An iterative approach to robust and integrated aircraft routing and crew scheduling
Computers and Operations Research
Robust Airline Scheduling Under Block-Time Uncertainty
Transportation Science
Integrated Airline Crew Pairing and Crew Assignment by Dynamic Constraint Aggregation
Transportation Science
Railway Rolling Stock Planning: Robustness Against Large Disruptions
Transportation Science
Solving a robust airline crew pairing problem with column generation
Computers and Operations Research
An Optimization Approach to Airline Integrated Recovery
Transportation Science
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Due to irregular operations, the crew cost at the end of a month is typically substantially higher than the crew cost projected in planning. We assume that the fleeting and the aircraft routing decisions have already been made. We present a model and a solution methodology that produces robust crew schedules in planning. Besides the objective of minimizing the crew cost, we introduce the objective of maximizing the number of move-up crews, i.e., the crews that can potentially be swapped in operations. To solve the resulting large-scale integer program, we use a combination of delayed column generation and Lagrangian relaxation. The restricted master problem is solved by means of Lagrangian relaxation and the “duals” of the restricted master problem, which are used in delayed column generation, and correspond to the Lagrangian multipliers. We report computational experiments that demonstrate the benefits of using the robust crew schedule instead of the traditional one. We evaluate various crew schedules by generating random disruptions and then running a crew recovery module. We compare solutions with respect to the direct crew cost and indirect costs such as uncovered legs, reserved crews, and deadheading. The main conclusion is that robustness leads to reduced operational crew cost; however, in planning the trade-off between the inflated direct crew cost and robustness needs to be exploited judicially.