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Analysis of Errors Due to Demand Data Aggregation in the Set Covering and Maximal Covering Location Problems
Author(s) -
Current John R.,
Schilling David A.
Publication year - 1990
Publication title -
geographical analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.773
H-Index - 65
eISSN - 1538-4632
pISSN - 0016-7363
DOI - 10.1111/j.1538-4632.1990.tb00199.x
Subject(s) - computer science , metric (unit) , set (abstract data type) , limit (mathematics) , binary number , scale (ratio) , mathematical optimization , data set , data mining , algorithm , mathematics , artificial intelligence , economics , geography , mathematical analysis , operations management , arithmetic , cartography , programming language
In this paper, we extend the concepts of demand data aggregation error to location problems involving coverage. These errors, which arise from losses in locational information, may lead to suboptimal location patterns. They are potentially more significant in covering problems than in p‐median problems because the distance metric is binary in covering problems. We examine the Hillsman and Rhoda (1978) Source A, B, and C errors, identify their coverage counterparts, and relate them to the cost and optimality errors that may result. Three rules are then presented which, when applied during data aggregation, will reduce these errors. The third rule will, in fact, eliminate all loss of locational information, but may also limit the amount of aggregation possible. Results of computational tests on a large‐scale problem are presented to demonstrate the performance of rule 3.