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Dynamic programming of flights based on stochastic demands

Published: 06 December 2015 Publication History

Abstract

The definition of the flights schedule is one of the major planning activities that are carried out in an airline. The result of the definition of this schedule has implications that transcend the operational sphere and becomes a determining factor to improve competitiveness in the sector of air transportation. The strategic nature of this activity implies that often is carried out so early, in a period where the forecast for the passenger demand is quite diffuse. This lead often to flight with an aircraft with empty seats, or to assign too many passengers to an specific flight. In this paper we present the DACRA algorithm. DACRA enables a continuous adaptation of the flights schedule to a modifications with respect to the forecasted passenger demand for a specific date. Also it assures that new generated solutions are operationally feasible.

References

[1]
Bazargan, Massoud. 2010. Airline Operations and Scheduling. Ashgate Publishing, Ltd.
[2]
Berge, M. E., and C. A. Hopperstad. 1993. "Demand Driven Dispatch: A Method for Dynamic Aircraft Capacity Assignment, Models and Algorithms." Operations Research 41 (1): 153--168.
[3]
Jiang, Hai. 2006. "Dynamic Airline Scheduling and Robust Airline Schedule De-Peaking."
[4]
Warburg, Valdemar, Troels Gotsæd Hansen, Allan Larsen, Hans Norman, and Erik Andersson. 2008. "Dynamic Airline Scheduling: An Analysis of the Potentials of Refleeting and Retiming." Journal of Air Transport Management 14 (4): 163--167.
  1. Dynamic programming of flights based on stochastic demands

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    cover image ACM Conferences
    WSC '15: Proceedings of the 2015 Winter Simulation Conference
    December 2015
    4051 pages
    ISBN:9781467397414

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    IEEE Press

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    Published: 06 December 2015

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    WSC '15
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    WSC '15: Winter Simulation Conference
    December 6 - 9, 2015
    California, Huntington Beach

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    WSC '15 Paper Acceptance Rate 202 of 296 submissions, 68%;
    Overall Acceptance Rate 3,413 of 5,075 submissions, 67%

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