Repository logo
  • English
  • Čeština
  • Deutsch
  • Español
  • Français
  • Gàidhlig
  • Latviešu
  • Magyar
  • Nederlands
  • Português
  • Português do Brasil
  • Suomi
  • Svenska
  • Türkçe
  • Қазақ
  • বাংলা
  • हिंदी
  • Ελληνικά
  • Log In
    or
    New user? Click here to register.
Repository logo

Repositorio Institucional de la Universidad de Murcia

Repository logoRepository logo
  • Communities & Collections
  • All of DSpace
  • Statistics
  • menu.section.collectors
  • menu.section.acerca
  • English
  • Čeština
  • Deutsch
  • Español
  • Français
  • Gàidhlig
  • Latviešu
  • Magyar
  • Nederlands
  • Português
  • Português do Brasil
  • Suomi
  • Svenska
  • Türkçe
  • Қазақ
  • বাংলা
  • हिंदी
  • Ελληνικά
  • Log In
    or
    New user? Click here to register.
  1. Home
  2. Browse by Subject

Browsing by Subject "Ranking Aggregation"

Now showing 1 - 1 of 1
Results Per Page
Sort Options
  • Loading...
    Thumbnail Image
    Publication
    Open Access
    An optimization-based approach to ranking aggregation with weak order outputs
    (2026) Aledo, Juan A.; Landete, Mercedes; Domínguez Sánchez, Concepción; Jaime Alcántara, Juan de Dios; Estadística e Investigación Operativa; Facultades de la UMU::Facultad de Matemáticas
    Rank aggregation problems combine individual orderings of a common set of items into a consensus ranking reflecting collective preferences. This paper introduces a general Integer Linear Programming (ILP) framework to model and solve aggregation problems whose solutions are weak orders (bucket orders). The framework provides a flexible and tractable architecture that incorporates structural and normative constraints required in practice. Within this setting, we develop several ILP formulations embedding additional structural requirements on the consensus bucket order, including a fixed number of buckets, predefined bucket sizes, top-$k$ constraints, and group-based fairness conditions. The formulations are modular and adaptable to different aggregation contexts. A particularly relevant case is the Optimal Bucket Order Problem (OBOP), for which we present the first exact mixed-integer linear programming formulation. We evaluate the models through computational experiments, comparing optimal solutions with the heuristics of Aledo et al.\ (2018) and assessing scalability on benchmark instances from the PrefLib library. Finally, we present a real-world case study on Spanish universities, where the proposed models aggregate competing rankings under structural and fairness constraints.

DSpace software copyright © 2002-2026 LYRASIS

  • Cookie settings
  • Accessibility
  • Send Feedback