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 Ruiz, Mercedes
Monge Ivars, Juan Francisco
Department:
Departamentos de la UMH::Estadística, Matemáticas e Informática
Issue Date:
2020-02-29
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
Keywords/Subjects:
Linear ordering problem
Rank aggregation problem
Bucket ordering problem
Metaheuristics
Type of document:
application/pdf
Access rights:
info:eu-repo/semantics/openAccess
Appears in Collections:
Artículos



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