2026-09-19T20:54:30Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/458002022-01-13T07:54:48Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Peter Belobaba.
Guo, Jingqiang Charles
Massachusetts Institute of Technology. Operations Research Center.
Massachusetts Institute of Technology. Operations Research Center
Sloan School of Management
2009-06-30T16:19:28Z
2009-06-30T16:19:28Z
2008
2008
http://hdl.handle.net/1721.1/45800
319062634
Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2008.
Includes bibliographical references (p. 69-71).
The growth of Low Fare Carriers (LFCs) has encouraged many airlines to remove fare restrictions (such as advance purchase requirements and Saturday-night stays) on many of their fare class products, leading to the simplification of fare structures in competitive markets. In the most extreme case, these markets have fare structures that are unrestricted; the fare class products differ only by price since they AL1 lack restrictions. In these unrestricted markets, passengers buy the lowest possible fare product since there are no longer any restrictions that prevent them from doing so. A forecasting method known as "Q-forecasting" takes into account the sell- up potential of passengers in forecasting the demand in each of the fare products in such markets. Sell-up occurs when passengers upon being denied their original fare class choice, decide to pay more for the next available fare class so long as the price remains below their maximum willingness to pay. Quantifying this sell-up potential either using estimated or input values is thus crucial in helping airlines increase revenues when competing in unrestricted fare markets. A simulation model known as the Passenger Origin-Destination Simulator (PODS) contains the following 3 sell-up estimation methods: (i) Direct Observation (DO), (ii) Forecast Prediction (FP), and (iii) Inverse Cumulative (IC). The goal of this thesis is thus to investigate and compare the revenue performance of the 3 sell-up estimation methods. These methods are tested in a 2-airline (consisting of AL1 and AL2) unrestricted network under different RM fare class optimization scenarios.
(cont.) Both estimated and input sell-up values are tested on AL1 whereas only input sell-up values are tested on AL2. The findings of the simulations indicate that using FP typically results in the highest revenues for AL1 among AL1 3 sell-up estimation methods. When compared against simple RM fare class threshold methods that do not consider sell-up, using FP results in up to a 3% revenue gain for AL1. Under some fare class optimization scenarios, using FP instead of input sell-up values even results in a revenue increase of close to 1%. These findings suggest that FP is robust enough under a range of fare class optimizers to be used by airlines as a sell-up estimator in unrestricted fare environments so as to raise revenues.
by Jingqiang Charles Guo.
S.M.
71 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
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http://dspace.mit.edu/handle/1721.1/7582
Operations Research Center.
Estimation of sell-up potential in airline revenue management systems
Thesis
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<Abstract>The growth of Low Fare Carriers (LFCs) has encouraged many airlines to remove fare restrictions (such as advance purchase requirements and Saturday-night stays) on many of their fare class products, leading to the simplification of fare structures in competitive markets. In the most extreme case, these markets have fare structures that are unrestricted; the fare class products differ only by price since they AL1 lack restrictions. In these unrestricted markets, passengers buy the lowest possible fare product since there are no longer any restrictions that prevent them from doing so. A forecasting method known as "Q-forecasting" takes into account the sell- up potential of passengers in forecasting the demand in each of the fare products in such markets. Sell-up occurs when passengers upon being denied their original fare class choice, decide to pay more for the next available fare class so long as the price remains below their maximum willingness to pay. Quantifying this sell-up potential either using estimated or input values is thus crucial in helping airlines increase revenues when competing in unrestricted fare markets. A simulation model known as the Passenger Origin-Destination Simulator (PODS) contains the following 3 sell-up estimation methods: (i) Direct Observation (DO), (ii) Forecast Prediction (FP), and (iii) Inverse Cumulative (IC). The goal of this thesis is thus to investigate and compare the revenue performance of the 3 sell-up estimation methods. These methods are tested in a 2-airline (consisting of AL1 and AL2) unrestricted network under different RM fare class optimization scenarios.</Abstract>
<Abstract>(cont.) Both estimated and input sell-up values are tested on AL1 whereas only input sell-up values are tested on AL2. The findings of the simulations indicate that using FP typically results in the highest revenues for AL1 among AL1 3 sell-up estimation methods. When compared against simple RM fare class threshold methods that do not consider sell-up, using FP results in up to a 3% revenue gain for AL1. Under some fare class optimization scenarios, using FP instead of input sell-up values even results in a revenue increase of close to 1%. These findings suggest that FP is robust enough under a range of fare class optimizers to be used by airlines as a sell-up estimator in unrestricted fare environments so as to raise revenues.</Abstract>
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