Efficient trip generation with a rule modeling system for crew scheduling problems
Journal of Systems and Software
Proceedings of the 35th conference on Winter simulation: driving innovation
Operational airline reserve crew planning
Journal of Scheduling
Duty-period-based network model for crew rescheduling in European airlines
Journal of Scheduling
A Stochastic Programming Approach to the Airline Crew Scheduling Problem
Transportation Science
Robust crew pairing for managing extra flights
Computers and Operations Research
Disruption management in the airline industry-Concepts, models and methods
Computers and Operations Research
Column Generation with Dynamic Duty Selection for Railway Crew Rescheduling
Transportation Science
A search-based approach to the railway rolling stock allocation problem
COCOA'10 Proceedings of the 4th international conference on Combinatorial optimization and applications - Volume Part II
Exact approaches for integrated aircraft fleeting and routing at TunisAir
Computational Optimization and Applications
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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An airline schedule rarely operates as planned. It is often disrupted by maintenance problems or severe weather conditions. In a typical day, several flights may be delayed or canceled, and aircraft and crews may miss the rest of their assigned flights. Airline coordinators have to find a minimal cost reassignment of aircraft and crews that satisfies all required safety rules, has little impact on passengers, and minimizes operational difficulties for the airline. The size of the entire schedule and the real-time nature of the problem rule out a full-scale optimization. It is necessary to reduce the complexity and the size of the problem before an optimization approach can be applied. In this paper, we focus on the problem of airline crew recovery. A new solution framework is developed, implemented, and tested. It provides, in almost real time, a recovery plan for reassigning crews to restore a disrupted crew schedule. Preprocessing techniques are applied to extract a subset of the schedule for rescheduling. A fast crew-pairing generator is built that enumerates feasible continuations of partially flown crew trips. Several branching strategies are presented that allow fast generation of integer solutions. We disturb the current schedule as little as possible, exploiting the fact that the planned schedule is optimal. The proposed framework has been implemented using tree-based data structures for efficient storage and data access. Computational results using a schedule from a major air carrier are presented.