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  <doc>
    <id>3323</id>
    <completedYear>2010</completedYear>
    <publishedYear>2010</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>232</pageFirst>
    <pageLast>241</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace>Avignon, France</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A Two-Stage Approach to WLAN Planning</title>
    <parentTitle language="eng">Proc. of the 8th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt’10)</parentTitle>
    <enrichment key="PeerReviewed">yes</enrichment>
    <author>Andreas Eisenblätter</author>
    <author>Hans-Florian Geerdes</author>
    <author>James Gross</author>
    <author>Oscar Puñal</author>
    <author>Jonas Schweiger</author>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="persons" number="schweiger">Schweiger, Jonas</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
  </doc>
  <doc>
    <id>1220</id>
    <completedYear/>
    <publishedYear>2010</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>149</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>masterthesis</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2011-02-23</completedDate>
    <publishedDate>2010-07-22</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Application of Multistage Stochastic Programming in Strategic Telecommunication Network Planning</title>
    <abstract language="eng">Telecommunication is fundamental for the information society. In both, the&#13;
private and the professional sector, mobile communication is nowadays taken&#13;
for granted. Starting primarily as a service for speech communication, data&#13;
service and mobile Internet access are now driving the evolution of network&#13;
infrastructure. In the year 2009, 19 million users generated over 33&#13;
million GB of traffic using mobile data services. The 3rd generation&#13;
networks (3G or UMTS) in Germany comprises over 39,000 base stations with&#13;
some 120,000 cells. From 1998 to 2008, the four network operators in&#13;
Germany invested over 33 billion Euros in their infrastructure. A careful&#13;
allocation of the resources is thus crucial for the profitability for a&#13;
network operator: a network should be dimensioned to match customers&#13;
demand. As this demand evolves over time, the infrastructure has to evolve&#13;
accordingly. The demand evolution is hard to predict and thus constitutes a&#13;
strong source of uncertainty. Strategic network planning has to take this&#13;
uncertainty into account, and the planned network evolution should adapt to&#13;
changing market conditions. The application of superior planning methods&#13;
under the consideration of uncertainty can improve the profitability of the&#13;
network and creates a competitive advantage. Multistage stochastic&#13;
programming is a suitable framework to model strategic telecommunication&#13;
network planning.&#13;
&#13;
&#13;
We present mathematical models and effective optimization procedures for&#13;
strategic cellular network design. The demand evolution is modeled as a&#13;
continuous stochastic process which is approximated by a discrete scenario&#13;
tree. A tree-stage approach is used for the construction of non-uniform&#13;
scenario trees that serve as input of the stochastic program.  The model is&#13;
calibrated by historical traffic observations.  A realistic system model of&#13;
UMTS radio cells is used that determines coverage areas and cell capacities&#13;
and takes signal propagation and interferences into account.  The network&#13;
design problem is formulated as a multistage stochastic mixed integer&#13;
linear program, which is solved using state-of-the-art commercial MIP&#13;
solvers.  Problem specific presolving is proposed to reduce the problem&#13;
size. Computational results on realistic data is presented.  Optimization&#13;
for the expected profit and the conditional value at risk are performed and&#13;
compared.</abstract>
    <identifier type="urn">urn:nbn:de:0297-zib-12206</identifier>
    <advisor>Martin Grötschel</advisor>
    <author>Jonas Schweiger</author>
    <submitter>-empty- (Opus4 user: )</submitter>
    <advisor>Werner Römisch</advisor>
    <submitter>Jonas Schweiger</submitter>
    <collection role="msc" number="90B18">Communication networks [See also 68M10, 94A05]</collection>
    <collection role="msc" number="90C15">Stochastic programming</collection>
    <collection role="msc" number="90C90">Applications of mathematical programming</collection>
    <collection role="collections" number="">Studienabschlussarbeiten</collection>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="persons" number="schweiger">Schweiger, Jonas</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
    <thesisPublisher>Zuse Institute Berlin (ZIB)</thesisPublisher>
    <thesisGrantor>Technische Universität Berlin</thesisGrantor>
    <file>https://opus4.kobv.de/opus4-zib/files/1220/Diplomarbeit_Final_href.pdf</file>
  </doc>
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