A dynamic programming algorithm based on expected revenue approximation for the network revenue management problem

Kuan-cheng Huang*, Yu Tung Liang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

23 Scopus citations

Abstract

Since American Airlines successfully applied revenue management (RM) to raise its revenue, RM has become a common technique in the airline industry. Due to the current hub-and-spoke operation of the airline industry, the focus of RM research has shifted from the traditional single-leg problem to the network-type problem. The mainstream approaches, bid price and virtual nesting, are faced with some limitations such as inaccuracy due to their suboptimal nature and operation interruption caused by the required updates. This study developed an algorithm to generate a seat control policy by approximating the expected revenue function in a dynamic programming (DP) model. In order to deal with the issue of dimensionality for the DP model in a network context, this study used a suitable parameterized function and a sampling concept to achieve the approximation. In the numerical experiment, the objective function value of the developed algorithm was very close to the one achieved by the optimal control. We believe that this approach can serve as an alternative to the current mainstream approaches for the network RM problem for airlines and will provide an inspiring concept for other types of multi-resource RM problems.

Original languageEnglish
Pages (from-to)333-341
Number of pages9
JournalTransportation Research Part E: Logistics and Transportation Review
Volume47
Issue number3
DOIs
StatePublished - 1 Jan 2011

Keywords

  • Dynamic programming
  • Revenue management
  • Seat control policy

Fingerprint Dive into the research topics of 'A dynamic programming algorithm based on expected revenue approximation for the network revenue management problem'. Together they form a unique fingerprint.

Cite this