Flight Network-Based Approach for Integrated Airline Recovery with Cruise Speed Control


Creative Commons License

ARIKAN U., GÜREL S. , Akturk M. S.

TRANSPORTATION SCIENCE, cilt.51, ss.1259-1287, 2017 (SCI İndekslerine Giren Dergi) identifier identifier

  • Cilt numarası: 51 Konu: 4
  • Basım Tarihi: 2017
  • Doi Numarası: 10.1287/trsc.2016.0716
  • Dergi Adı: TRANSPORTATION SCIENCE
  • Sayfa Sayıları: ss.1259-1287

Özet

Airline schedules are generally tight and fragile to disruptions. Disruptions can have severe effects on existing aircraft routings, crew pairings, and passenger itineraries that lead to high delay and recovery costs. A recovery approach should integrate the recovery decisions for all entities (aircraft, crew, passengers) in the system as recovery decisions about an entity directly affect the others' schedules. Because of the size of airline flight networks and the requirement for quick recovery decisions, the integrated airline recovery problem is highly complex. In the past decade, an increasing effort has been made to integrate passenger and crew related recovery decisions with aircraft recovery decisions both in practice and in the literature. In this paper, we develop a new flight network based representation for the integrated airline recovery problem. Our approach is based on the flow of each aircraft, crew member, and passenger through the flight network of the airline. The proposed network structure allows common recovery decisions such as departure delays, aircraft/crew rerouting, passenger reaccommodation, ticket cancellations, and flight cancellations. Furthermore, we can implement aircraft cruise speed (flight time) decisions on the flight network. For the integrated airline recovery problem defined over this network, we propose a conic quadratic mixed integer programming formulation that can be solved in reasonable CPU times for practical size instances. Moreover, we place a special emphasis on passenger recovery. In addition to aggregation and approximation methods, our model allows explicit modeling of passengers and evaluating a more realistic measure of passenger delay costs. Finally, we propose methods based on the proposed network representation to control the problem size and to deal with large airline networks.