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Methods, Models, and GIS

Heuristics in Spatial Analysis: A Genetic Algorithm for Coverage Maximization

, &
Pages 698-711
Received 01 Mar 2008
Accepted 01 Jul 2008
Published online: 04 Sep 2009
 

Many government agencies and corporations face locational decisions, such as where to locate fire stations, postal facilities, nature reserves, computer centers, bank branches, and so on. To reach such location-related decisions, geographical information systems (GIS) are essential for providing access to spatial data and analysis tools. Moreover, geographic insights can be gained from GIS as they enable capabilities for better reflecting problems of interest in location modeling. The resulting models can be complex, however, and hence computationally challenging to solve. This article examines an important model for regional service coverage maximization. This model is solved heuristically using a genetic algorithm. The new heuristic innovatively incorporates problem-specific knowledge by exploring the geographical structure of the problem under study. Comparative application results demonstrate important nuances of the new genetic algorithm, enhancing overall performance.

Muchas agencias del gobierno y empresas enfrentan decisiones locacionales, por ejemplo, las que se refieren a dónde ubicar estaciones de bomberos, oficinas postales, reservas naturales, centros de cómputo y sucursales bancarias, entre otras. Los sistemas de información geográfica (SIG) son esenciales a la hora de decidir sobre cosas relacionadas con localización, ya que aquéllos proveen acceso a datos espaciales y a las herramientas para analizarlos. Más todavía, con los SIG se puede ahondar en perspicacia geográfica en cuanto éstos nos capacitan para mejor captar los problemas de interés en modelaje locacional. Sin embargo, los modelos resultantes pueden ser complejos y por lo mismo convertir su solución en retos computacionales. En este artículo se examina un importante modelo sobre maximización de una cobertura regional de servicios. Este modelo es resuelto heurísticamente mediante el uso de un algoritmo genético. Innovadoramente, la nueva heurística toma en consideración el conocimiento centrado en un problema específico, explorando la estructura geográfica de la cuestión que se estudia. La aplicación comparada de los resultados demuestra importantes matices del nuevo algoritmo genético, mejorando en todo el desempeño

Acknowledgments

This material is based upon work supported by the National Science Foundation under Grant No. 0518967 (awarded to Murray). Any opinions, findings, and conclusions are those of the authors and do not necessarily reflect the views of the National Science Foundation.

Notes

1. A problem that is NP-hard means that no algorithm has been discovered yet to solve it in polynomial time in the worst case (Garey and Johnson 1979 Garey, M. R. and Johnson, D. S. 1979. Computers and intractability: A guide to the theory of NP-completeness, New York: Freeman.  [Google Scholar]). It is likely that such an efficient algorithm might not exist.

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