Expected Future Value Decomposition Based Bid Price Generation for Large-Scale Network Revenue Management

This paper studies a multistage stochastic programming (SP) model for large-scale network revenue management. The authors solve the model by means of the so-called expected future value (EFV) decomposition via scenario analysis, estimating the impact of the decisions made at a given stage on the objective function value related to the future stages. The EFV curves are used to define bid prices on bundles of resources directly, as opposed to the traditional additive approach. The authors compare their revenues to those obtained by additive bid prices, such as the bid prices derived from the deterministic equivalent model (DEM) of the compact representation of the SP model. The authors' computational experience shows that the revenues obtained by their approach are better for middle-range values of the load factor of demand, whereas the differences among all the approaches they have tested are insignificant for extreme values. Moreover, the authors' approach requires significantly less computation time than does the optimization of DEM by plain use of optimization engines. Problem instances with 72 pairs of bundle-fare classes have been solved in less than one minute, with 800 pairs in less than five minutes, and with 4,000 pairs in less than one hour. The time taken by DEM was, in general, of one order of magnitude higher. Finally, for the three largest problem instances, and after two hours, the expected revenue returned by DEM was below that obtained by EFV by 13.47%, 17.14%, and 38.94%, respectively.

• Record URL:
  https://doi.org/10.1287/trsc.1120.0422
• Record URL:
  http://transci.journal.informs.org/content/47/2/181.abstract
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/1767714
• Supplemental Notes:
  • Abstracts reprinted with permission of INFORMS (Institute for Operations Research and the Management Sciences, http://www.informs.org).
• Authors:
  • Escudero, L F
  • Monge, J F
  • Morales, D Romero
  • Wang, J
• Publication Date: 2013-5

Language

• English

Media Info

• Media Type: Print
• Features: Figures; References; Tables;
• Pagination: pp 181-197
• Serial:
  • Transportation Science
  • Volume: 47
  • Issue Number: 2
  • Publisher: Institute for Operations Research and the Management Sciences (INFORMS)
  • ISSN: 0041-1655
  • Serial URL: http://transci.journal.informs.org/

Subject/Index Terms

• TRT Terms: Bids; Dynamic programming; Expected value; Forecasting; Networks; Stochastic programming; Yield management
• Subject Areas: Administration and Management; Planning and Forecasting; Transportation (General); I10: Economics and Administration; I72: Traffic and Transport Planning;

Filing Info

• Accession Number: 01482834
• Record Type: Publication
• Files: TRIS
• Created Date: May 16 2013 11:34AM