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Title:
Demand Point Aggregation for Planar Covering Location Models | Annals of Operations Research
Description:
The covering location problem seeks the minimum number of facilities such that each demand point is within some given radius of its nearest facility. Such a model finds application mostly in locating emergency types of facilities. Since the problem is NP-hard in the plane, a common practice is to aggregate the demand points in order to reduce the computational burden. Aggregation makes the size of the problem more manageable but also introduces error. Identifying and controlling the magnitude of the error is the subject of this study. We suggest several aggregation methods with a priori error bounds, and conduct experiments to compare their performance. We find that the manner by which infeasibility is measured greatly affects the best choice of an aggregation method.
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Keywords {🔍}
google, scholar, article, location, aggregation, research, problems, francis, analysis, operations, problem, models, demand, covering, facility, journal, point, methods, pmedian, lowe, privacy, cookies, content, data, annals, error, discrete, network, applications, springer, rayco, publish, search, access, chapter, york, errors, geographical, pcenter, science, siam, european, information, log, find, emirfarinas, emergency, related, discover, theory,
Topics {✒️}
month download article/chapter unweighted p-center problems maximum covering models p-median location problems constrained location models multi-facility location problems worst-case aggregation analysis related subjects demand point aggregation p-median problem p-center problem privacy choices/manage cookies m-center problem discrete location theory m-center problems geometric location problems demand data aggregation full article pdf model finds application location-allocation models demand point operations research aims network location problems locating emergency types network flow problems �row-column aggregation scope submit manuscript measured greatly affects �aggregation error bounds �siting emergency services conditions privacy policy �asymptotically optimal aggregation priori error bounds article emir-farinas european economic area accepting optional cookies covering problems main content log discrete location aggregation problem penalty function approach location models check access instant access journal finder publish facility location operations research 18 operations research operations research 40 demand points
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headline:Demand Point Aggregation for Planar Covering Location Models
description:The covering location problem seeks the minimum number of facilities such that each demand point is within some given radius of its nearest facility. Such a model finds application mostly in locating emergency types of facilities. Since the problem is NP-hard in the plane, a common practice is to aggregate the demand points in order to reduce the computational burden. Aggregation makes the size of the problem more manageable but also introduces error. Identifying and controlling the magnitude of the error is the subject of this study. We suggest several aggregation methods with a priori error bounds, and conduct experiments to compare their performance. We find that the manner by which infeasibility is measured greatly affects the best choice of an aggregation method.
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location models
demand point aggregation
covering problem
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headline:Demand Point Aggregation for Planar Covering Location Models
description:The covering location problem seeks the minimum number of facilities such that each demand point is within some given radius of its nearest facility. Such a model finds application mostly in locating emergency types of facilities. Since the problem is NP-hard in the plane, a common practice is to aggregate the demand points in order to reduce the computational burden. Aggregation makes the size of the problem more manageable but also introduces error. Identifying and controlling the magnitude of the error is the subject of this study. We suggest several aggregation methods with a priori error bounds, and conduct experiments to compare their performance. We find that the manner by which infeasibility is measured greatly affects the best choice of an aggregation method.
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