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    <pageFirst>105</pageFirst>
    <pageLast>116</pageLast>
    <pageNumber>12</pageNumber>
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    <issue/>
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    <publisherName>SciTePress - Science and Technology Publications</publisherName>
    <publisherPlace>Setúbal, Portugal</publisherPlace>
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    <completedDate>2025-12-10</completedDate>
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    <title language="eng">Solving the three-dimensional beacon placement problem using constraint-based methods, large neighborhood search, and evolutionary algorithms</title>
    <abstract language="eng">With the increasing prevalence of large building complexes, indoor localization is becoming an area of growing significance. In critical situations, such as emergencies in factories or care facilities, the ability to locate a person quickly can be a matter of life and death. One possibility for localization are Bluetooth beacons, which are either attached to the person or in rooms. We pursue the latter approach, whereby the beacon signals are used to determine the position of a receiving device, e.g. a mobile phone. At this, the use of a sufficient number of beacons in the building must be ensured in order to guarantee adequate coverage. However, to minimize costs, it is equally important to avoid placing unnecessary beacons. This creates a challenging optimization problem that this paper addresses through three distinct approaches: constraint programming, large neighborhood search, and evolutionary algorithms. Using simulated three-dimensional buildings, we test and evaluate these m ethods, ultimately providing a practical and efficient approach applicable to real-world building environments.</abstract>
    <parentTitle language="eng">Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics : Volume 1 : ICINCO</parentTitle>
    <identifier type="doi">10.5220/0013724500003982</identifier>
    <identifier type="isbn">978-989-758-770-2</identifier>
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    <author>
      <firstName>Sven</firstName>
      <lastName>Löffler</lastName>
    </author>
    <editor>
      <firstName>Giuseppina</firstName>
      <lastName>Gini</lastName>
    </editor>
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      <firstName>Petra</firstName>
      <lastName>Hofstedt</lastName>
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    <author>
      <firstName>Viktoria</firstName>
      <lastName>Abbenhaus</lastName>
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    <editor>
      <firstName>Radu-Emil</firstName>
      <lastName>Precup</lastName>
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    <author>
      <firstName>George</firstName>
      <lastName>Assaf</lastName>
    </author>
    <editor>
      <firstName>Dimitar P.</firstName>
      <lastName>Filev</lastName>
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    <author>
      <firstName>Petra</firstName>
      <lastName>Hofstedt</lastName>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Constraint Programming</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>AI Algorithms</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Beacon Placement</value>
    </subject>
    <collection role="institutes" number="1207">FG Programmiersprachen und Compilerbau</collection>
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  <doc>
    <id>37003</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
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    <language>eng</language>
    <pageFirst>91</pageFirst>
    <pageLast>103</pageLast>
    <pageNumber>13</pageNumber>
    <edition/>
    <issue/>
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    <publisherName>Springer Nature Switzerland</publisherName>
    <publisherPlace>Cham</publisherPlace>
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    <completedDate>2025-12-10</completedDate>
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    <title language="eng">A hybrid constraint-based, greedy, and local search approach for the transshipment problem</title>
    <abstract language="eng">The efficient resolution of logistics problems, particularly those aimed at minimizing costs and reducing environmental impact, represents a critical challenge in our globalized world. A prominent example of such problems is the Transshipment Problem, which seeks to determine the most cost-effective paths from sources (e.g., producers) through transshipment points to sinks (e.g., customers). Approaches to addressing this problem range from greedy algorithms, which may rapidly yield locally optimal solutions, to constraint-based methods that, given sufficient resources and computation time, can identify globally optimal solutions. In this study, we propose a hybrid approach that integrates greedy strategies into the solution process of constraint modeling for the Transshipment Problem. This integration aims to expedite the discovery of high-quality initial solutions while preserving the global optimization capabilities inherent in constraint-based search methods. To validate the effectiveness of this new hybrid approach, we conducted an extensive series of experiments, which demonstrate its significant advantages in solving the Transshipment Problem compared to both a conventional constraint model and pure greedy methods.</abstract>
    <parentTitle language="eng">KI 2025 : advances in artificial intelligence : 48th German Conference on AI, Potsdam, Germany, September 16–19, 2025 : proceedings</parentTitle>
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      <firstName>Petra</firstName>
      <lastName>Hofstedt</lastName>
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      <value>Transshipment problem</value>
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