Coverage area maximization with Parallel Simulated Annealing
This study provides a system that determines where to locate disks-like services of radius so that they globally cover as much as possible a region of demand. It is an NP-hard problem with notorious applications in the facility location field when locating multiple warning sirens, cellular towers, r...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repositorio: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:recercat.cat:10256/21510 |
| Acceso en línea: | http://hdl.handle.net/10256/21510 |
| Access Level: | acceso abierto |
| Palabra clave: | Matemàtica aplicada Geometria computacional Applied mathematics Computational geometry Algorismes paral·lels Parallel algorithms |
| Sumario: | This study provides a system that determines where to locate disks-like services of radius so that they globally cover as much as possible a region of demand. It is an NP-hard problem with notorious applications in the facility location field when locating multiple warning sirens, cellular towers, radio stations, or pollution sensors covering as much area as possible of a city or geographical region. The region of demand is assumed to be delimited by a general polygonal domain, and the resolution strategy relies on a parallel simulated annealing optimization technique based on a suitable perturbation strategy and a probabilistic estimation of the area of the polygonal region covered by the disks in (2) time. The system provides a good enough location for the disks starting from an arbitrary initial solution with very reasonable running times. The proposal is experimentally tested by visualizing the solutions, analyzing and contrasting their quality, and studying the computational efficiency of the entire strategy |
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