Essec\Faculty\Model\Contribution {#2216
  #_index: "academ_contributions"
  #_id: "7943"
  #_source: array:26 [
    "id" => "7943"
    "slug" => "choice-rules-with-size-constraints-for-multiple-criteria-decision-making"
    "yearMonth" => "2004-01"
    "year" => "2004"
    "title" => "Choice Rules with Size Constraints for Multiple Criteria Decision Making"
    "description" => "ALFANDARI, L. (2004). <i>Choice Rules with Size Constraints for Multiple Criteria Decision Making</i>. ESSEC Business School."
    "authors" => array:1 [
      0 => array:3 [
        "name" => "ALFANDARI Laurent"
        "bid" => "B00000901"
        "slug" => "alfandari-laurent"
      ]
    ]
    "ouvrage" => ""
    "keywords" => []
    "updatedAt" => "2020-12-17 21:00:33"
    "publicationUrl" => null
    "publicationInfo" => array:3 [
      "pages" => null
      "volume" => null
      "number" => null
    ]
    "type" => array:2 [
      "fr" => "Documents de travail"
      "en" => "Working Papers"
    ]
    "support_type" => array:2 [
      "fr" => "Editeur"
      "en" => "Publisher"
    ]
    "countries" => array:2 [
      "fr" => null
      "en" => null
    ]
    "abstract" => array:2 [
      "fr" => "Dans les méthodes de surclassement pour l'Aide Multicritère à la Décision, la comparaison par paire des actions potentielles aboutit le plus souvent à la construction d'une relation floue de préférence. Dans ce papier, nous proposons d'étendre la relation binaire de préférence aux sous-ensembles d'actions, ce qui nous conduit à déterminer une fonction de score associée à chaque sous-ensemble. Une procédure de choix basée sur la maximisation du score sous contrainte de cardinalité est étudiée. Nous montrons que cette procédure de choix équivaut à résoudre de façon séquentielle des problèmes combinatoires de localisation bien connus. Afin de comparer cette nouvelle approche à la méthode classique de recherche de noyaux, nous analysons la propriété de stabilité du sous-ensemble choisi et discutons de la pertinence de cette contrainte."
      "en" => "In outranking methods for Multiple Criteria Decision Making (MCDM), pair-wise comparisons of alternatives are often summarized through a fuzzy preference relation. In this paper, the binary preference relation is extended to pairs of subsets of alternatives in order to define on this basis a scoring function over subsets. A choice rule based on maximizing score under size constraints is studied, which turns to formulate as solving a sequence of classical location problems. For comparison with the kernel approach, the interior stability property of the selected subset is discussed and analized."
    ]
    "authors_fields" => array:2 [
      "fr" => "Systèmes d'Information, Data Analytics et Opérations"
      "en" => "Information Systems, Data Analytics and Operations"
    ]
    "indexedAt" => "2024-12-21T16:21:43.000Z"
    "docTitle" => "Choice Rules with Size Constraints for Multiple Criteria Decision Making"
    "docSurtitle" => "Working Papers"
    "authorNames" => "<a href="/cv/alfandari-laurent">ALFANDARI Laurent</a>"
    "docDescription" => "<span class="document-property-authors">ALFANDARI Laurent</span><br><span class="document-property-authors_fields">Information Systems, Data Analytics and Operations</span> | <span class="document-property-year">2004</span>"
    "keywordList" => ""
    "docPreview" => "<b>Choice Rules with Size Constraints for Multiple Criteria Decision Making</b><br><span>2004-01 | Working Papers </span>"
    "docType" => "research"
    "publicationLink" => "<a href="#" target="_blank">Choice Rules with Size Constraints for Multiple Criteria Decision Making</a>"
  ]
  +lang: "en"
  +"_type": "_doc"
  +"_score": 8.758286
  +"parent": null
}