2026-09-20T02:46:37Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/581812022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Vivek F. Farias.
Park, Joongwoo Brian
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2010-09-02T14:55:00Z
2010-09-02T14:55:00Z
2010
2010
http://hdl.handle.net/1721.1/58181
635955071
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 53-54).
Network revenue management is the practice of using optimal decision policies to increase revenues by controlling limited quantities of multiple resources' availability and prices over finite time. It is widely practiced in capacity-constrained service industries such as the airlines, hotels, car rentals, and cruise-lines. A variety of control methods has been introduced for network resource capacity control problem. We propose a clustering method to improve approximation quality. By clustering the legs of the network, one can find tighter upperbound than leg-wise decomposition with loss of computation speed due to larger state space. We have shown that there is more than 6% revenue improvement opportunity by finding the right clustering. With local interchange heuristic and generic heuristics, finding a locally optimal clustering can be done in faster time. We also introduce risk-aversion in network revenue management. We have investigated risk-aversion on network revenue management and also study the impact of risk-aversion parameters in the optimization model on relative revenue-risk performance.
by Joongwoo Brian Park.
S.M.
54 p.
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
Electrical Engineering and Computer Science.
Capacity control in network revenue management : clustering and risk-aversion
Thesis
application/pdf
false
Publication
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="12ad2cbb-d1ed-4792-9850-49cc408b879a">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Language>eng</Language>
<Title>Capacity control in network revenue management : clustering and risk-aversion</Title>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2010</PublicationDate>
<Authors>
<Author>
<DisplayName>Park, Joongwoo Brian</DisplayName>
<Affiliation>
<OrgUnit>
</OrgUnit>
</Affiliation>
</Author>
</Authors>
<Editors>
</Editors>
<Publishers>
<Publisher>
<DisplayName>Massachusetts Institute of Technology</DisplayName>
<OrgUnit />
</Publisher>
</Publishers>
<License>http://dspace.mit.edu/handle/1721.1/7582</License>
<Keyword>Electrical Engineering and Computer Science.</Keyword>
<Abstract>Network revenue management is the practice of using optimal decision policies to increase revenues by controlling limited quantities of multiple resources' availability and prices over finite time. It is widely practiced in capacity-constrained service industries such as the airlines, hotels, car rentals, and cruise-lines. A variety of control methods has been introduced for network resource capacity control problem. We propose a clustering method to improve approximation quality. By clustering the legs of the network, one can find tighter upperbound than leg-wise decomposition with loss of computation speed due to larger state space. We have shown that there is more than 6% revenue improvement opportunity by finding the right clustering. With local interchange heuristic and generic heuristics, finding a locally optimal clustering can be done in faster time. We also introduce risk-aversion in network revenue management. We have investigated risk-aversion on network revenue management and also study the impact of risk-aversion parameters in the optimization model on relative revenue-risk performance.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>