TY - JOUR
T1 - Heuristics in spatial analysis
T2 - A genetic algorithm for coverage maximization
AU - Tong, Daoqin
AU - Murray, Alan
AU - Xiao, Ningchuan
N1 - Funding Information:
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.
Copyright:
Copyright 2009 Elsevier B.V., All rights reserved.
PY - 2009/10
Y1 - 2009/10
N2 - 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 problemspecific 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.
AB - 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 problemspecific 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.
KW - Facility location
KW - Genetic algorithm
KW - Heuristics
KW - Maximal coverage
KW - Spatial analysis
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U2 - 10.1080/00045600903120594
DO - 10.1080/00045600903120594
M3 - Article
AN - SCOPUS:69949122537
SN - 0004-5608
VL - 99
SP - 698
EP - 711
JO - Annals of the Association of American Geographers
JF - Annals of the Association of American Geographers
IS - 4
ER -