Essec\Faculty\Model\Contribution {#2216
  #_index: "academ_contributions"
  #_id: "14804"
  #_source: array:26 [
    "id" => "14804"
    "slug" => "benders-decomposition-for-the-discrete-ordered-median-problem"
    "yearMonth" => "2024-09"
    "year" => "2024"
    "title" => "Benders decomposition for the discrete ordered median problem"
    "description" => "LJUBIC, I., POZO, M.A., PUERTO, J. et TORREJÓN, A. (2024). Benders decomposition for the discrete ordered median problem. <i>European Journal of Operational Research</i>, 317(3), pp. 858-874."
    "authors" => array:4 [
      0 => array:3 [
        "name" => "LJUBIC Ivana"
        "bid" => "B00683004"
        "slug" => "ljubic-ivana"
      ]
      1 => array:1 [
        "name" => "Pozo Miguel A."
      ]
      2 => array:1 [
        "name" => "Puerto Justo"
      ]
      3 => array:1 [
        "name" => "Torrejón Alberto"
      ]
    ]
    "ouvrage" => ""
    "keywords" => array:4 [
      0 => "Discrete location"
      1 => "Ordered median optimization"
      2 => "Benders decomposition"
      3 => "Branch-and-Benders-cut"
    ]
    "updatedAt" => "2024-10-31 13:51:19"
    "publicationUrl" => "https://doi.org/10.1016/j.ejor.2024.04.030"
    "publicationInfo" => array:3 [
      "pages" => "858-874"
      "volume" => "317"
      "number" => "3"
    ]
    "type" => array:2 [
      "fr" => "Articles"
      "en" => "Journal articles"
    ]
    "support_type" => array:2 [
      "fr" => "Revue scientifique"
      "en" => "Scientific journal"
    ]
    "countries" => array:2 [
      "fr" => null
      "en" => null
    ]
    "abstract" => array:2 [
      "fr" => """
        Ordered median optimization has been proven to be a powerful tool to generalize many well-known problems from the literature. In Location Theory, the Discrete Ordered Median Problem (DOMP) is a facility location problem where clients are first ranked according to their allocation cost to the nearest open facility, and then these costs are multiplied by a suitable weight vector \n
        . That way, DOMP generalizes many well-known discrete location problems including \n
        -median, \n
        -center or centdian. In this article, we also allow negative entries of \n
        , allowing us to derive models for better addressing equity and fairness in facility location, for modeling obnoxious facility location problems or for including other client preference models. We present new mixed integer programming models for DOMP along with algorithmic enhancements for solving the DOMP to optimality using mixed integer programming techniques. Specifically, starting from state-of-the-art formulations from the literature, we present several Benders decomposition reformulations applied to them. Using these approaches, new state-of-the-art results have been obtained for different ordered weighting vectors.
        """
      "en" => """
        Ordered median optimization has been proven to be a powerful tool to generalize many well-known problems from the literature. In Location Theory, the Discrete Ordered Median Problem (DOMP) is a facility location problem where clients are first ranked according to their allocation cost to the nearest open facility, and then these costs are multiplied by a suitable weight vector \n
        . That way, DOMP generalizes many well-known discrete location problems including \n
        -median, \n
        -center or centdian. In this article, we also allow negative entries of \n
        , allowing us to derive models for better addressing equity and fairness in facility location, for modeling obnoxious facility location problems or for including other client preference models. We present new mixed integer programming models for DOMP along with algorithmic enhancements for solving the DOMP to optimality using mixed integer programming techniques. Specifically, starting from state-of-the-art formulations from the literature, we present several Benders decomposition reformulations applied to them. Using these approaches, new state-of-the-art results have been obtained for different ordered weighting vectors.
        """
    ]
    "authors_fields" => array:2 [
      "fr" => "Systèmes d'Information, Data Analytics et Opérations"
      "en" => "Information Systems, Data Analytics and Operations"
    ]
    "indexedAt" => "2024-11-21T11:21:49.000Z"
    "docTitle" => "Benders decomposition for the discrete ordered median problem"
    "docSurtitle" => "Journal articles"
    "authorNames" => "<a href="/cv/ljubic-ivana">LJUBIC Ivana</a>, Pozo Miguel A., Puerto Justo, Torrejón Alberto"
    "docDescription" => "<span class="document-property-authors">LJUBIC Ivana, Pozo Miguel A., Puerto Justo, Torrejón Alberto</span><br><span class="document-property-authors_fields">Information Systems, Data Analytics and Operations</span> | <span class="document-property-year">2024</span>"
    "keywordList" => "<a href="#">Discrete location</a>, <a href="#">Ordered median optimization</a>, <a href="#">Benders decomposition</a>, <a href="#">Branch-and-Benders-cut</a>"
    "docPreview" => "<b>Benders decomposition for the discrete ordered median problem</b><br><span>2024-09 | Journal articles </span>"
    "docType" => "research"
    "publicationLink" => "<a href="https://doi.org/10.1016/j.ejor.2024.04.030" target="_blank">Benders decomposition for the discrete ordered median problem</a>"
  ]
  +lang: "en"
  +"_type": "_doc"
  +"_score": 8.953466
  +"parent": null
}