A new mathematical model and a Lagrangean decomposition for the point-feature cartographic label placement problem

Authors:
Geraldo R. Mauri;Glaydston M. Ribeiro;Luiz A. N. Lorena
Affiliations:
Federal University of Espírito Santo-UFES, Alto Universitário s/n, Guararema, Alegre/ES 29500-000, Brazil;Federal University of Espírito Santo-UFES, R. Humberto de Almeida Francklin, São Mateus/ES 29933-415, Brazil;National Institute for Space Research-INPE, Av. dos Astronautas, 1758, Jardim da Granja, São José dos Campos/SP 12210-970, Brazil
Venue:
Computers and Operations Research
Year:
2010

Citing 7
Cited 2

The solution of large 0–1 integer programming problems encountered in automated cartography

Operations Research
Placing text labels on maps and diagrams

Graphics gems IV
A decomposition method for quadratic zero-one programming

Management Science
An empirical study of algorithms for point-feature label placement

ACM Transactions on Graphics (TOG)
Tabu Search Heuristic for Point-Feature Cartographic Label Placement

Geoinformatica
Lagrangean relaxation with clusters for point-feature cartographic label placement problems

Computers and Operations Research
A greedy randomized adaptive search procedure for the point-feature cartographic label placement

Computers & Geosciences

Relax-and-fix decomposition technique for solving large scale grid-based location problems

Computers and Industrial Engineering
Dispersion for the point-feature cartographic label placement problem

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.01

Visualization

Abstract

This paper proposes a 0-1 integer linear programming model for the point-feature cartographic label placement problem based on labeling of the largest number of free labels. In addition, one non-trivial valid inequality is presented to strengthen this proposed model. Even with the strengthened model, a commercial solver was not able to solve a representative sample of known instances presented in the literature. Thus, we also present a Lagrangean decomposition technique based on graph partitioning. Our added approaches established optimal solutions for practically all the used instances and the results significantly improved the ones presented in recent studies concerning the problem.