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The linear ordering problem with clusters: a new partial ranking

The linear ordering problem with clusters: a new partial ranking
Alcaraz Soria, Javier
García Nové, Eva María
Landete, Mercedes  
Monge Ivars, Juan Francisco
Departamentos de la UMH::Estadística, Matemáticas e Informática
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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
Linear ordering problem
Rank aggregation problem
Bucket ordering problem
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Artículos Estadística, Matemáticas e Informática

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