### Resumé

Originalsprog | Engelsk |
---|---|

Tidsskrift | Transportation Science |

Vol/bind | 47 |

Udgave nummer | 2 |

Sider (fra-til) | 181-197 |

ISSN | 0041-1655 |

DOI | |

Status | Udgivet - 2013 |

Udgivet eksternt | Ja |

### Emneord

- Network revenue management
- Scenario trees
- Stochastic dynamic programming
- Expected future value curves
- Nonadditive bid prices
- Load factor of demand

### Citer dette

*Transportation Science*,

*47*(2), 181-197. https://doi.org/10.1287/trsc.1120.0422

}

*Transportation Science*, bind 47, nr. 2, s. 181-197. https://doi.org/10.1287/trsc.1120.0422

**Expected Future Value Decomposition Based Bid Price Generation for Large-Scale Network Revenue Management.** / Escudero, Laureano F.; Monge, Juan Francisco; Romero Morales, Dolores ; Wang, J.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › peer review

TY - JOUR

T1 - Expected Future Value Decomposition Based Bid Price Generation for Large-Scale Network Revenue Management

AU - Escudero, Laureano F.

AU - Monge, Juan Francisco

AU - Romero Morales, Dolores

AU - Wang, J.

PY - 2013

Y1 - 2013

N2 - 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.

AB - 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.

KW - Network revenue management

KW - Scenario trees

KW - Stochastic dynamic programming

KW - Expected future value curves

KW - Nonadditive bid prices

KW - Load factor of demand

U2 - 10.1287/trsc.1120.0422

DO - 10.1287/trsc.1120.0422

M3 - Journal article

VL - 47

SP - 181

EP - 197

JO - Transportation Science

JF - Transportation Science

SN - 0041-1655

IS - 2

ER -