A Linear Programming approach for robust network revenue management in the airline industry

The classical revenue management problem consists of allocating a fixed network capacity to different customer classes, so as to maximize revenue. This area has been widely applied in service industries that are characterized by a fixed perishable capacity, such as airlines, cruises, hotels, etc.It is traditionally assumed that demand is uncertain, but can be characterized as a stochastic process (See Talluri and van Ryzin (2005) for a review of the revenue management models). In practice, however, airlines have limited demand information and are unable to fully characterize demand stochastic processes. Robust optimization methods have been proposed to overcome this modeling challenge. Under robust optimization framework, demand is only assumed to lie within a polyhedral uncertainty set (Lan et al. (2008); Perakis and Roels (2010)). In this paper, the authors consider the multi-fare, network revenue management problem for the case demand information is limited (i.e. the only information available is lower/upper bounds on demand). Under this interval uncertainty, the authors characterize the robust optimal booking limit policy by use of minimax regret criterion. They present an LP (Linear Programming) solvable mathematical program for the maximum regret so their model is able to solve large-scale problems for practical use. A genetic algorithm is proposed to find the booking limit control to minimize the maximum regret. The authors provide computational experiments and compare their methods to existing ones. The results demonstrate the effectiveness of this robust approach.

• Record URL:
  • https://doi.org/10.1016/j.jairtraman.2020.101979
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0969699720305615
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/31005945
• Supplemental Notes:
  • © 2020 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • An, Jaehyung
  • Mikhaylov, Alexey
  • Jung, Sang-Uk
• Publication Date: 2021-3

Language

• English

Media Info

• Media Type: Web
• Features: References; Tables;
• Serial:
  • Journal of Air Transport Management
  • Volume: 91
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0969-6997
  • Serial URL: http://www.sciencedirect.com/science/journal/09696997

Subject/Index Terms

• TRT Terms: Airlines; Demand; Fares; Linear programming; Yield management
• Subject Areas: Administration and Management; Aviation; Economics; Finance;

Filing Info

• Accession Number: 01759427
• Record Type: Publication
• Files: TRIS
• Created Date: Nov 30 2020 5:32PM