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New results for optimization in stochastic networks
Descriptive
TitleInfo
Title
New results for optimization in stochastic networks
Name
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Unuvar
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Merve
NamePart
(type = date)
1985-
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Merve Unuvar
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author
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Prekopa
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Andras
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Andras Prekopa
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Advisory Committee
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chair
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Boros
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Endre
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Endre Boros
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Advisory Committee
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internal member
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BEN-ISRAEL
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ADI BEN-ISRAEL
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Advisory Committee
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internal member
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Baykal-Gursoy
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Melike
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Melike Baykal-Gursoy
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Advisory Committee
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internal member
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Subasi
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Mine
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Mine Subasi
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Advisory Committee
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outside member
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Rutgers University
Role
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degree grantor
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Graduate School - New Brunswick
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school
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Text
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theses
OriginInfo
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2012
DateOther
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2012-10
Place
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xx
Language
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eng
Abstract
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We are interested in single commodity stochastic network design problems under probabilistic constraint with discrete and continuous random variables. We use a stochastic programming model under probabilistic constraint (also called a chance-constrained model) to study these problems. The problem addressed in this research is how to find minimum cost optimal capacities at the nodes and/or arcs subject to the constraint that the demands should be met on a prescribed probability level (reliability constraint). In our first problem formulation, we formulate the reliability constraint in terms of the Gale-Hoffman feasibility inequalities. In latter formulations, we allow system to meet the demand at least $k$-out-of-$n$ and consecutive $k$-out-of-$n$ periods. The number of reliability constraints, in both cases, increases exponentially with the size of the nodes and therefore we identify the redundant constraints and reduce their number with elimination methods. Even with the reduced number of inequalities, it is not simple to solve probabilistic constrained stochastic network problems due to the large number of efficient points that satisfy the probabilistic condition. To overcome the size limitation of the problem, we develop a new theorem for efficient point generation in the case when the random variables are discrete, and we use hybrid cutting plane / supporting hyperplane algorithm in the case when the random variables are continuous.
Subject
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Topic
Operations Research
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Title
Rutgers University Electronic Theses and Dissertations
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ETD_4268
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electronic resource
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application/pdf
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text/xml
Extent
viii, 141 p. : ill.
Note
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Ph.D.
Note
(type = bibliography)
Includes bibliographical references
Note
(type = vita)
Includes vita
Note
(type = statement of responsibility)
by Merve Unuvar
Subject
(authority = ETD-LCSH)
Topic
Stochastic processes
Subject
(authority = ETD-LCSH)
Topic
Structural optimization
Identifier
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http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000067007
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Title
Graduate School - New Brunswick Electronic Theses and Dissertations
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rucore19991600001
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NjNbRU
Identifier
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doi:10.7282/T39P30D9
Genre
(authority = ExL-Esploro)
ETD doctoral
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Rights
RightsDeclaration
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The author owns the copyright to this work.
RightsHolder
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Name
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Unuvar
GivenName
Merve
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Copyright Holder
RightsEvent
Type
Permission or license
DateTime
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(point = start)
2012-09-23 06:52:51
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Name
Merve Unuvar
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Affiliation
Rutgers University. Graduate School - New Brunswick
AssociatedObject
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License
Name
Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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