Essec\Faculty\Model\Contribution {#2216
  #_index: "academ_contributions"
  #_id: "13811"
  #_source: array:26 [
    "id" => "13811"
    "slug" => "a-survey-on-bilevel-optimization-under-uncertainty"
    "yearMonth" => "2023-12"
    "year" => "2023"
    "title" => "A survey on bilevel optimization under uncertainty"
    "description" => "BECK, Y., LJUBIC, I. et SCHMIDT, M. (2023). A survey on bilevel optimization under uncertainty. <i>European Journal of Operational Research</i>, 311(2), pp. 401-426."
    "authors" => array:3 [
      0 => array:3 [
        "name" => "LJUBIC Ivana"
        "bid" => "B00683004"
        "slug" => "ljubic-ivana"
      ]
      1 => array:1 [
        "name" => "BECK Yasmine"
      ]
      2 => array:1 [
        "name" => "SCHMIDT Martin"
      ]
    ]
    "ouvrage" => ""
    "keywords" => array:5 [
      0 => "(R) uncertainty modeling"
      1 => "Bilevel optimization"
      2 => "Optimization under uncertainty"
      3 => "Bounded rationality"
      4 => "Survey"
    ]
    "updatedAt" => "2024-10-31 13:51:19"
    "publicationUrl" => "https://doi.org/10.1016/j.ejor.2023.01.008"
    "publicationInfo" => array:3 [
      "pages" => "401-426"
      "volume" => "311"
      "number" => "2"
    ]
    "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" => "Bilevel optimization is a very active field of applied mathematics. The main reason is that bilevel optimization problems can serve as a powerful tool for modeling hierarchical decision making processes. This ability, however, also makes the resulting problems challenging to solve—both in theory and practice. Fortunately, there have been significant algorithmic advances in the field of bilevel optimization so that we can solve much larger and also more complicated problems today compared to what was possible to solve two decades ago. This results in more and more challenging bilevel problems that researchers try to solve today. This survey gives a detailed overview of one of these more challenging classes of bilevel problems: bilevel optimization under uncertainty. We review the classic ways of addressing uncertainties in bilevel optimization using stochastic or robust techniques. Moreover, we highlight that the sources of uncertainty in bilevel optimization are much richer than for usual, i.e., single-level, problems since not only the problem’s data can be uncertain but also the (observation of the) decisions of the two players can be subject to uncertainty. We thus also review the field of bilevel optimization under limited observability, the area of problems considering only near-optimal decisions, and discuss intermediate solution concepts between the optimistic and pessimistic cases. Finally, we also review the rich literature on applications studied using uncertain bilevel problems such as in energy, for interdiction games and security applications, in management sciences, and networks."
      "en" => "Bilevel optimization is a very active field of applied mathematics. The main reason is that bilevel optimization problems can serve as a powerful tool for modeling hierarchical decision making processes. This ability, however, also makes the resulting problems challenging to solve—both in theory and practice. Fortunately, there have been significant algorithmic advances in the field of bilevel optimization so that we can solve much larger and also more complicated problems today compared to what was possible to solve two decades ago. This results in more and more challenging bilevel problems that researchers try to solve today. This survey gives a detailed overview of one of these more challenging classes of bilevel problems: bilevel optimization under uncertainty. We review the classic ways of addressing uncertainties in bilevel optimization using stochastic or robust techniques. Moreover, we highlight that the sources of uncertainty in bilevel optimization are much richer than for usual, i.e., single-level, problems since not only the problem’s data can be uncertain but also the (observation of the) decisions of the two players can be subject to uncertainty. We thus also review the field of bilevel optimization under limited observability, the area of problems considering only near-optimal decisions, and discuss intermediate solution concepts between the optimistic and pessimistic cases. Finally, we also review the rich literature on applications studied using uncertain bilevel problems such as in energy, for interdiction games and security applications, in management sciences, and networks."
    ]
    "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" => "A survey on bilevel optimization under uncertainty"
    "docSurtitle" => "Journal articles"
    "authorNames" => "<a href="/cv/ljubic-ivana">LJUBIC Ivana</a>, BECK Yasmine, SCHMIDT Martin"
    "docDescription" => "<span class="document-property-authors">LJUBIC Ivana, BECK Yasmine, SCHMIDT Martin</span><br><span class="document-property-authors_fields">Information Systems, Data Analytics and Operations</span> | <span class="document-property-year">2023</span>"
    "keywordList" => "<a href="#">(R) uncertainty modeling</a>, <a href="#">Bilevel optimization</a>, <a href="#">Optimization under uncertainty</a>, <a href="#">Bounded rationality</a>, <a href="#">Survey</a>"
    "docPreview" => "<b>A survey on bilevel optimization under uncertainty</b><br><span>2023-12 | Journal articles </span>"
    "docType" => "research"
    "publicationLink" => "<a href="https://doi.org/10.1016/j.ejor.2023.01.008" target="_blank">A survey on bilevel optimization under uncertainty</a>"
  ]
  +lang: "en"
  +"_type": "_doc"
  +"_score": 8.953466
  +"parent": null
}