Name
Title | ||
|---|---|---|
Solving the airline crew recovery problem by a genetic algorithm with local improvement |
Abstract | ||
|---|---|---|
Within the complex and dynamic environment of the airline industry, any disturbance to normal operations has dramatic impact,
and usually imposes high additional costs. Because of irregular events during day-to-day operations, airline crew schedules
are rarely operated as planned in practice. Therefore, disrupted schedules should be recovered with as small changes as possible.
In this article, we propose a genetic algorithm (GA) based approach, in which disrupted flights are reassigned within an evolutionary
process. Because of the slow convergence rate achieved by conventional GA, a special local improvement procedure is applied
in this approach. Computational results are reported for several disruption scenarios on real-life instances from a medium-sized
European airline. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/BF02944311 | Operational Research |
Keywords | DocType | Volume |
airline crew recovery,meta-heuristic,airline crew scheduling,disruption management,genetic algorithm,airline crew rescheduling | Journal | 5 |
Issue | ISSN | Citations |
2 | 1866-1505 | 6 |
PageRank | References | Authors |
0.42 | 10 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yufeng Guo | 1 | 95 | 11.62 |
| Leena Suhl | 2 | 243 | 26.87 |
| Markus P. Thiel | 3 | 25 | 1.70 |