2026-09-19T21:55:11Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/351192022-01-13T07:54:23Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Peter P. Belobaba.
Dar, Maital
Massachusetts Institute of Technology. Dept. of Civil and Environmental Engineering.
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
2006-12-18T20:43:37Z
2006-12-18T20:43:37Z
2006
2006
http://hdl.handle.net/1721.1/35119
71661524
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2006.
Includes bibliographical references (p. 124-129).
In the wake of contemporary widespread fare simplification in many major airline markets, this thesis is concerned with the possibilities and the potential for airline revenue management in less-differentiated fare environments. Traditional revenue management has relied upon the assumption that independent demands exist for different fare class products, and can be forecast as such. However, in less-differentiated fare environments this assumption has been shown to lead to "spiral-down" in revenues. Hence, in this thesis, seat inventory control methods are simulated in less-differentiated fare environments and their relative performances are compared. The methods tested are: EMSRb-based Fare Class Yield Management (FCYM); Heuristic Bid Price (HBP); Displacement Adjusted Virtual Nesting (DAVN); and Probabilistic Bid Price (ProBP). Each of the methods is tested in conjunction with two different demand forecasting philosophies: the traditional pickup (or moving average) forecaster which is based on the assumption of independent demands; and a hybrid forecasting method based on the notion that there is one demand for flexible products and another demand for the cheapest product. The methods are simulated in two different competitive airline network environments: a symmetric network with simplified fares; and a more complex non-symmetric network with mixed fare structures. Simulation shows that the performance of all four revenue management methods suffers in less-differentiated fare environments if they continue to use traditional forecasting. Methods that forecast demand at the path level see inflated forecasts for more expensive products, leading them to reject too much lower-class demand; methods that forecast demand at the leg level see diminished forecasts for the more expensive products, leading them to accept too much lower-class demand. The efficacy of FCYM improves in less-differentiated fare environments, providing a gain of about 19% over "First Come First Served" revenues (as compared to the 6% gains seen previously), nevertheless, fare product simplification still results in overall network revenue losses of around 16%. Incremental gains from O-D control when using traditional forecasting range from 0.44% to 1.93%.o over FCYM. In contrast, when the new hybrid forecaster is used, revenue management performance improves significantly, and all methods provide larger revenue gains in all competitive network environments. Revenues under FCYM are now 1.7-2.6% higher than when traditional forecasting is used. When using hybrid forecasting, the incremental gains from O-D control now range from 2.6% to 4% over FCYM.
by Maital Dar.
S.M.
129 p.
8563549 bytes
8569866 bytes
application/pdf
application/pdf
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Civil and Environmental Engineering.
Modelling the performance of revenue management systems in different competitive environments
Thesis
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<Abstract>In the wake of contemporary widespread fare simplification in many major airline markets, this thesis is concerned with the possibilities and the potential for airline revenue management in less-differentiated fare environments. Traditional revenue management has relied upon the assumption that independent demands exist for different fare class products, and can be forecast as such. However, in less-differentiated fare environments this assumption has been shown to lead to "spiral-down" in revenues. Hence, in this thesis, seat inventory control methods are simulated in less-differentiated fare environments and their relative performances are compared. The methods tested are: EMSRb-based Fare Class Yield Management (FCYM); Heuristic Bid Price (HBP); Displacement Adjusted Virtual Nesting (DAVN); and Probabilistic Bid Price (ProBP). Each of the methods is tested in conjunction with two different demand forecasting philosophies: the traditional pickup (or moving average) forecaster which is based on the assumption of independent demands; and a hybrid forecasting method based on the notion that there is one demand for flexible products and another demand for the cheapest product. The methods are simulated in two different competitive airline network environments: a symmetric network with simplified fares; and a more complex non-symmetric network with mixed fare structures. Simulation shows that the performance of all four revenue management methods suffers in less-differentiated fare environments if they continue to use traditional forecasting. Methods that forecast demand at the path level see inflated forecasts for more expensive products, leading them to reject too much lower-class demand; methods that forecast demand at the leg level see diminished forecasts for the more expensive products, leading them to accept too much lower-class demand. The efficacy of FCYM improves in less-differentiated fare environments, providing a gain of about 19% over "First Come First Served" revenues (as compared to the 6% gains seen previously), nevertheless, fare product simplification still results in overall network revenue losses of around 16%. Incremental gains from O-D control when using traditional forecasting range from 0.44% to 1.93%.o over FCYM. In contrast, when the new hybrid forecaster is used, revenue management performance improves significantly, and all methods provide larger revenue gains in all competitive network environments. Revenues under FCYM are now 1.7-2.6% higher than when traditional forecasting is used. When using hybrid forecasting, the incremental gains from O-D control now range from 2.6% to 4% over FCYM.</Abstract>
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