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  <doc>
    <id>5901</id>
    <completedYear/>
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    <language>eng</language>
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    <edition/>
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    <completedDate>--</completedDate>
    <publishedDate>2016-02-04</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Towards an accurate solution of wireless network design problems</title>
    <abstract language="eng">The optimal design of wireless networks has been widely studied in the literature and many optimization models have been proposed over the years. However, most models directly include the signal-to-interference ratios representing service coverage conditions. This leads&#13;
to mixed-integer linear programs with constraint matrices containing tiny&#13;
coefficients that vary widely in their order of magnitude. These formulations are known to be challenging even for state-of-the-art solvers: the&#13;
standard numerical precision supported by these solvers is usually not sufficient to reliably guarantee feasible solutions. Service coverage errors are thus commonly present. Though these numerical issues are known&#13;
and become evident even for small-sized instances, just a very limited number of papers has tried to tackle them, by mainly investigating alternative non-compact formulations in which the sources of numerical instabilities are eliminated. In this work, we explore a new approach by investigating how recent advances in exact solution algorithms for linear&#13;
and mixed-integer programs over the rational numbers can be applied to analyze and tackle the numerical difficulties arising in wireless network design models.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-59010</identifier>
    <enrichment key="SubmissionStatus">accepted for publication</enrichment>
    <enrichment key="SourceTitle">accepted for publication in Proceedings of the 4th International Symposium on Combinatorial Optimization (ISCO)</enrichment>
    <enrichment key="AcceptedDate">2016-04-11</enrichment>
    <author>Fabio D'Andreagiovanni</author>
    <submitter>Fabio D'Andreagiovanni</submitter>
    <author>Ambros Gleixner</author>
    <series>
      <title>ZIB-Report</title>
      <number>16-12</number>
    </series>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="institutes" number="mip">Mathematical Optimization Methods</collection>
    <collection role="institutes" number="tele">Mathematics of Telecommunication</collection>
    <collection role="projects" number="BMBF-ROBUKOM">BMBF-ROBUKOM</collection>
    <collection role="projects" number="ECMath-MI4">ECMath-MI4</collection>
    <collection role="projects" number="MODAL-SynLab">MODAL-SynLab</collection>
    <collection role="projects" number="VINO">VINO</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/5901/ZR-16-12_DAndreagiovanniGleixner_AccurateWirelessNetworkDesign.pdf</file>
  </doc>
  <doc>
    <id>5892</id>
    <completedYear/>
    <publishedYear>2016</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>283</pageFirst>
    <pageLast>298</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>9597</volume>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">An (MI)LP-based Primal Heuristic for 3-Architecture Connected Facility Location in Urban Access Network Design</title>
    <abstract language="eng">We investigate the 3-architecture Connected Facility Location Problem arising in the design of urban telecommunication access networks integrating wired and wireless technologies. We propose an original optimization model for the problem that includes additional variables and constraints to take into account wireless signal coverage represented through signal-to-interference ratios. Since the problem can prove very challenging even for modern state-of-the art optimization solvers, we propose to solve it by an original primal heuristic that combines a probabilistic fixing procedure, guided by peculiar Linear Programming relaxations, with an exact MIP heuristic, based on a very large neighborhood search. Computational experiments on a set of realistic instances show that our heuristic can find solutions associated with much lower optimality gaps than a state-of-the-art solver.</abstract>
    <parentTitle language="eng">EvoApplications: European Conference on the Applications of Evolutionary Computation. Applications of Evolutionary Computation. 19th European Conference, EvoApplications 2016, Porto, Portugal, March 30 -- April 1, 2016, Proceedings, Part I</parentTitle>
    <identifier type="doi">10.1007/978-3-319-31204-0_19</identifier>
    <enrichment key="Series">Lecture Notes in Computer Science</enrichment>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-57139</enrichment>
    <author>Fabio D'Andreagiovanni</author>
    <submitter>Fabio D'Andreagiovanni</submitter>
    <author>Fabian Mett</author>
    <author>Jonad Pulaj</author>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="institutes" number="tele">Mathematics of Telecommunication</collection>
    <collection role="projects" number="ECMath-MI4">ECMath-MI4</collection>
    <collection role="projects" number="VINO">VINO</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
  </doc>
  <doc>
    <id>5547</id>
    <completedYear/>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
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    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2015-06-24</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Towards the fast and robust optimal design of Wireless Body Area Networks</title>
    <abstract language="eng">Wireless body area networks are wireless sensor networks whose adoption has recently emerged and spread in important healthcare applications, such as the remote monitoring of health conditions of patients. A major issue associated with the deployment of such networks is represented by energy consumption: in general, the batteries of the sensors cannot be easily replaced and recharged, so containing the usage of energy by a rational design of the network and of the routing is crucial. Another issue is represented by traffic uncertainty: body sensors may produce data at a variable rate that is not exactly known in advance, for example because the generation of data is event-driven. Neglecting traffic uncertainty may lead to wrong design and routing decisions, which may compromise the functionality of the network and have very bad effects on the health of the patients. In order to address these issues, in this work we propose the first robust optimization model for jointly optimizing the topology and the routing in body area networks under traffic uncertainty. Since the problem may result challenging even for a state-of-the-art optimization solver, we propose an original optimization algorithm that exploits suitable linear relaxations to guide a randomized fixing of the variables, supported by an exact large variable neighborhood search.&#13;
Experiments on realistic instances indicate that our algorithm performs better than a state-of-the-art solver, fast producing solutions associated with&#13;
improved optimality gaps.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="doi">10.1016/j.asoc.2015.04.037</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-55475</identifier>
    <enrichment key="PeerReviewed">Yes</enrichment>
    <enrichment key="SourceTitle">Appeared in: Applied Soft Computing 2015</enrichment>
    <author>Fabio D'Andreagiovanni</author>
    <submitter>Fabio D'Andreagiovanni</submitter>
    <author>Antonella Nardin</author>
    <series>
      <title>ZIB-Report</title>
      <number>15-34</number>
    </series>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="institutes" number="tele">Mathematics of Telecommunication</collection>
    <collection role="projects" number="ECMath-MI4">ECMath-MI4</collection>
    <collection role="projects" number="VINO">VINO</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/5547/ZR-15-34_DAndreagiovanni_robustBodySensorNetworks.pdf</file>
  </doc>
  <doc>
    <id>5548</id>
    <completedYear/>
    <publishedYear>2015</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>971</pageFirst>
    <pageLast>982</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>37</volume>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Towards the fast and robust optimal design of Wireless Body Area Networks</title>
    <abstract language="eng">Wireless body area networks are wireless sensor networks whose adoption has recently emerged and spread in important healthcare applications, such as the remote monitoring of health conditions of patients. A major issue associated with the deployment of such networks is represented by energy consumption: in general, the batteries of the sensors cannot be easily replaced and recharged, so containing the usage of energy by a rational design of the network and of the routing is crucial. Another issue is represented by traffic uncertainty: body sensors may produce data at a variable rate that is not exactly known in advance, for example because the generation of data is event-driven. Neglecting traffic uncertainty may lead to wrong design and routing decisions, which may compromise the functionality of the network and have very bad effects on the health of the patients. In order to address these issues, in this work we propose the first robust optimization model for jointly optimizing the topology and the routing in body area networks under traffic uncertainty. Since the problem may result challenging even for a state-of-the-art optimization solver, we propose an original optimization algorithm that exploits suitable linear relaxations to guide a randomized fixing of the variables, supported by an exact large variable neighborhood search. Experiments on realistic instances indicate that our algorithm performs better than a state-of-the-art solver, fast producing solutions associated with improved optimality gaps.</abstract>
    <parentTitle language="eng">Applied Soft Computing</parentTitle>
    <identifier type="doi">10.1016/j.asoc.2015.04.037</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-55475</enrichment>
    <author>Fabio D'Andreagiovanni</author>
    <submitter>Fabio D'Andreagiovanni</submitter>
    <author>Antonella Nardin</author>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="institutes" number="tele">Mathematics of Telecommunication</collection>
    <collection role="projects" number="ECMath-MI4">ECMath-MI4</collection>
    <collection role="projects" number="VINO">VINO</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
  </doc>
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