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Strategies for managing the flexible capacity in the airline industry

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  • Ebru K. Bish
  • Rawee Suwandechochai
  • Douglas R. Bish

Abstract

The ability to effectively match supply and demand under uncertainty can result in significant revenue benefits in the airline industry. We study the benefits of a Demand Driven Swapping (DDS) approach that takes advantage of the flexibilities in the system and dynamically swaps aircraft as departures near and more accurate demand information is obtained. We analyze the effectiveness of different DDS strategies, characterized by their frequency (how often the swapping decision is revised), in hedging against demand uncertainty. Swapping aircraft several weeks prior to departures will not cause much disturbance to revenue management and operations, but will be based on highly uncertain demands. On the other hand, revising the swapping decision later will decrease the possibility of bad swaps, but at a higher cost of disrupting airport services and operations. Our objective is to provide guidelines on how the flexible (swappable) capacity should be managed in the system. We study analytical models to gain insights into the critical parameters that affect the revenue benefits of the different swapping strategies. Our study determines the conditions under which each of the different DDS strategies is effective. We complement our analysis by testing the proposed DDS strategies on a set of flight legs, using data obtained from United Airlines. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2004.

Suggested Citation

  • Ebru K. Bish & Rawee Suwandechochai & Douglas R. Bish, 2004. "Strategies for managing the flexible capacity in the airline industry," Naval Research Logistics (NRL), John Wiley & Sons, vol. 51(5), pages 654-685, August.
  • Handle: RePEc:wly:navres:v:51:y:2004:i:5:p:654-685
    DOI: 10.1002/nav.20019
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    Cited by:

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    4. Rujeerapaiboon, Napat & Zhong, Yuanguang & Zhu, Dan, 2023. "Resilience of long chain under disruption," European Journal of Operational Research, Elsevier, vol. 309(2), pages 597-615.
    5. Christina Büsing & Daniel Kadatz & Catherine Cleophas, 2019. "Capacity Uncertainty in Airline Revenue Management: Models, Algorithms, and Computations," Transportation Science, INFORMS, vol. 53(2), pages 383-400, March.
    6. Yiting Xing & Ling Li & Zhuming Bi & Marzena Wilamowska‐Korsak & Li Zhang, 2013. "Operations Research (OR) in Service Industries: A Comprehensive Review," Systems Research and Behavioral Science, Wiley Blackwell, vol. 30(3), pages 300-353, May.
    7. Catherine Cleophas & Daniel Kadatz & Sebastian Vock, 2017. "Resilient revenue management: a literature survey of recent theoretical advances," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 16(5), pages 483-498, October.
    8. Hai Jiang & Cynthia Barnhart, 2009. "Dynamic Airline Scheduling," Transportation Science, INFORMS, vol. 43(3), pages 336-354, August.
    9. Hanif D. Sherali & Xiaomei Zhu, 2008. "Two-Stage Fleet Assignment Model Considering Stochastic Passenger Demands," Operations Research, INFORMS, vol. 56(2), pages 383-399, April.

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