Essec\Faculty\Model\Contribution {#2233
  #_index: "academ_contributions"
  #_id: "1198"
  #_source: array:26 [
    "id" => "1198"
    "slug" => "fast-machine-reassignment"
    "yearMonth" => "2016-07"
    "year" => "2016"
    "title" => "Fast Machine Reassignment"
    "description" => "BUTELLE, F., ALFANDARI, L., COTI, C., FINTA, L., LÉTOCART, L. et PLATEAU, G. (2016). Fast Machine Reassignment. <i>Annals of Operations Research</i>, 242(1), pp. 130-160."
    "authors" => array:6 [
      0 => array:3 [
        "name" => "ALFANDARI Laurent"
        "bid" => "B00000901"
        "slug" => "alfandari-laurent"
      ]
      1 => array:1 [
        "name" => "BUTELLE F."
      ]
      2 => array:1 [
        "name" => "COTI C."
      ]
      3 => array:1 [
        "name" => "FINTA L."
      ]
      4 => array:1 [
        "name" => "LÉTOCART L."
      ]
      5 => array:1 [
        "name" => "PLATEAU G."
      ]
    ]
    "ouvrage" => ""
    "keywords" => array:5 [
      0 => "Generalized Assignment"
      1 => "Adaptive Variable Neighborhood Search"
      2 => "Simulated Annealing"
      3 => "Hyper-Heuristic"
      4 => "Cooperative Parallel Search"
    ]
    "updatedAt" => "2021-02-02 16:16:18"
    "publicationUrl" => "https://link.springer.com/article/10.1007/s10479-015-2082-3"
    "publicationInfo" => array:3 [
      "pages" => "130-160"
      "volume" => "242"
      "number" => "1"
    ]
    "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" => "This paper proposes a new method for solving the Machine Reassignment Problem in a very short computational time. The problem has been proposed by Google as subject of the Challenge ROADEF/EURO 2012. The Machine Reassignment Problem consists in looking for a reassignment of processes to machines in order to minimize a complex objective function, subject to a rich set of constraints including multidimensional resource, conflict and dependency constraints. In this study, a cooperative search approach is presented for machine reassignment. This approach uses two components: Adaptive Variable Neighbourhood Search and Simulated Annealing based Hyper-Heuristic, running in parallel on two threads and exchanging solutions. Both algorithms employ a rich set of heuristics and a learning mechanism to select the best neighborhood/move type during the search process. The cooperation mechanism acts as a multiple restart which gets triggered whenever a new better solution is achieved by a thread and then shared with the other thread. Computational results on the Challenge instances as well as instances of a Generalized Assignment-like problem are given to show the relevance of the chosen methods and the high benefits of cooperation."
      "en" => "This paper proposes a new method for solving the Machine Reassignment Problem in a very short computational time. The problem has been proposed by Google as subject of the Challenge ROADEF/EURO 2012. The Machine Reassignment Problem consists in looking for a reassignment of processes to machines in order to minimize a complex objective function, subject to a rich set of constraints including multidimensional resource, conflict and dependency constraints. In this study, a cooperative search approach is presented for machine reassignment. This approach uses two components: Adaptive Variable Neighbourhood Search and Simulated Annealing based Hyper-Heuristic, running in parallel on two threads and exchanging solutions. Both algorithms employ a rich set of heuristics and a learning mechanism to select the best neighborhood/move type during the search process. The cooperation mechanism acts as a multiple restart which gets triggered whenever a new better solution is achieved by a thread and then shared with the other thread. Computational results on the Challenge instances as well as instances of a Generalized Assignment-like problem are given to show the relevance of the chosen methods and the high benefits of cooperation."
    ]
    "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" => "Fast Machine Reassignment"
    "docSurtitle" => "Articles"
    "authorNames" => "<a href="/cv/alfandari-laurent">ALFANDARI Laurent</a>, BUTELLE F., COTI C., FINTA L., LÉTOCART L., PLATEAU G."
    "docDescription" => "<span class="document-property-authors">ALFANDARI Laurent, BUTELLE F., COTI C., FINTA L., LÉTOCART L., PLATEAU G.</span><br><span class="document-property-authors_fields">Systèmes d'Information, Data Analytics et Opérations</span> | <span class="document-property-year">2016</span>"
    "keywordList" => "<a href="#">Generalized Assignment</a>, <a href="#">Adaptive Variable Neighborhood Search</a>, <a href="#">Simulated Annealing</a>, <a href="#">Hyper-Heuristic</a>, <a href="#">Cooperative Parallel Search</a>"
    "docPreview" => "<b>Fast Machine Reassignment</b><br><span>2016-07 | Articles </span>"
    "docType" => "research"
    "publicationLink" => "<a href="https://link.springer.com/article/10.1007/s10479-015-2082-3" target="_blank">Fast Machine Reassignment</a>"
  ]
  +lang: "fr"
  +"_type": "_doc"
  +"_score": 9.269113
  +"parent": null
}