Please use this identifier to cite or link to this item:
https://hdl.handle.net/11000/6277
The linear ordering problem with clusters: a new partial ranking
Title: The linear ordering problem with clusters: a new partial ranking |
Authors: Alcaraz Soria, Javier García Nové, Eva María Landete, Mercedes Monge Ivars, Juan Francisco |
Department: Departamentos de la UMH::Estadística, Matemáticas e Informática |
Issue Date: 2020-02-29 |
URI: http://hdl.handle.net/11000/6277 |
Abstract:
The linear ordering problem is among core problems in combinatorial optimization.
There is a squared non-negative matrix and the goal is to find the permutation of
rows and columns which maximizes the sum of superdiagonal values. In this paper,
we consider that columns of the matrix belong to different clusters and that the goal
is to order the clusters. We introduce a new approach for the case when exactly one
representative is chosen from each cluster. The new problem is called the linear
ordering problem with clusters and consists of both choosing a representative for
each cluster and a permutation of these representatives, so that the sum of superdiagonal
values of the sub-matrix induced by the representatives is maximized. A
combinatorial linear model for the linear ordering problem with clusters is given,
and eventually, a hybrid metaheuristic is carefully designed and developed. Computational
results illustrate the performance of the model as well as the effectiveness of
the metaheuristic
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Keywords/Subjects: Linear ordering problem Rank aggregation problem Bucket ordering problem Metaheuristics |
Knowledge area: Análisis |
Type of document: info:eu-repo/semantics/article |
Access rights: info:eu-repo/semantics/openAccess |
DOI: https://doi.org/10.1007/s11750-020-00552-3 |
Appears in Collections: Artículos Estadística, Matemáticas e Informática
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