<div class="csl-bib-body">
<div class="csl-entry">Jatschka, T. (2022). <i>Computational optimization approaches for distributing service points for mobility applications and smart charging of electric vehicles</i> [Dissertation, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2022.107309</div>
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dc.identifier.uri
https://doi.org/10.34726/hss.2022.107309
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/135900
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dc.description.abstract
For many business models in the mobility domain an optimal distribution of service points in a customer community is needed. Examples are charging stations of electric vehicles (EVs), bicycle sharing stations, battery swapping stations, or repair stations. Two main challenges are to get the necessary data about the community and environment in order to estimate user demands, local constraints of potential locations, and other properties and to identify optimal service station locations based on these data. Traditionally, these two tasks are considered in a separated fashion. Obtaining input data for the optimization step in a classical way essentially always is inherently incomplete and error prone for larger practical scenarios since manifold aspects play roles in complex, often non-obvious ways, and not all of them can be captured with appropriate estimations of their impacts. In this thesis three different problems are investigated. As an introduction to distributing service points for mobility applications, the first problem focuses on the challenge of identifying optimal locations for battery swapping stations of electric scooters under the assumption that demand information is already known. The second problem is more generic but therefore considers user interaction for obtaining demand information interleaved with the optimization. Instead of estimating customer demands upfront, customers are incorporated directly into the optimization process, i.e., users can interact with the optimization algorithm by expressing their preferences for where to best place service points. Finally we consider the problem of scheduling the charging of EVs at a single charging station in which the maximum power at which a vehicle can be charged depends on the current state-of-charge of the vehicle.
en
dc.language
English
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dc.language.iso
en
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dc.rights.uri
http://rightsstatements.org/vocab/InC/1.0/
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dc.subject
Heuristic optimization
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dc.subject
Location planning
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dc.subject
Cooperative optimization
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dc.subject
Preference learning
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dc.subject
Machine Learning
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dc.subject
Electric vehicles
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dc.subject
Charging
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dc.subject
Scheduling
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dc.subject
Mixed Integer Linear Programming
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dc.title
Computational optimization approaches for distributing service points for mobility applications and smart charging of electric vehicles
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dc.type
Thesis
en
dc.type
Hochschulschrift
de
dc.rights.license
In Copyright
en
dc.rights.license
Urheberrechtsschutz
de
dc.identifier.doi
10.34726/hss.2022.107309
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dc.contributor.affiliation
TU Wien, Österreich
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dc.rights.holder
Thomas Jatschka
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dc.publisher.place
Wien
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tuw.version
vor
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tuw.thesisinformation
Technische Universität Wien
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tuw.publication.orgunit
E192 - Institut für Logic and Computation
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dc.type.qualificationlevel
Doctoral
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dc.identifier.libraryid
AC16698906
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dc.description.numberOfPages
158
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dc.thesistype
Dissertation
de
dc.thesistype
Dissertation
en
tuw.author.orcid
0000-0003-1885-8905
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dc.rights.identifier
In Copyright
en
dc.rights.identifier
Urheberrechtsschutz
de
tuw.advisor.staffStatus
staff
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tuw.advisor.orcid
0000-0002-3293-177X
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item.languageiso639-1
en
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item.openairetype
doctoral thesis
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item.grantfulltext
open
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item.fulltext
with Fulltext
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item.cerifentitytype
Publications
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item.mimetype
application/pdf
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item.openairecristype
http://purl.org/coar/resource_type/c_db06
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item.openaccessfulltext
Open Access
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crisitem.author.dept
E192-01 - Forschungsbereich Algorithms and Complexity