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

L. F. Escudero (), J. F. Monge (), D. Romero Morales () and J. Wang ()
Additional contact information
L. F. Escudero: Departamento de Estadística e Investigación Operativa, Universidad Rey Juan Carlos, Madrid, Spain
J. F. Monge: Centro de Investigación Operativa, Universidad Miguel Hernández, Elche (Alicante), Spain
D. Romero Morales: Saïd Business School, University of Oxford, Oxford, United Kingdom
J. Wang: Saïd Business School, University of Oxford, Oxford, United Kingdom

Transportation Science, 2013, vol. 47, issue 2, 181-197

Abstract: This paper studies a multistage stochastic programming (SP) model for large-scale network revenue management. We 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. We compare our 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. Our computational experience shows that the revenues obtained by our approach are better for middle-range values of the load factor of demand, whereas the differences among all the approaches we have tested are insignificant for extreme values. Moreover, our 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.

Keywords: network revenue management; scenario trees; stochastic dynamic programming; expected future value curves; nonadditive bid prices; load factor of demand (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://dx.doi.org/10.1287/trsc.1120.0422 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ortrsc:v:47:y:2013:i:2:p:181-197

Access Statistics for this article

More articles in Transportation Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().