Paper Info
Title
The demand partitioning method for reducing aggregation errors in p-median problems
Abstract
The demand partitioning method for reducing aggregation errors in the solution of aggregated p -median problems is introduced in this paper. The method consists of eliminating source A and B aggregation errors using the Current and Schilling (Geographical Analysis 1987;19:95–110) weighting method and then partitioning the basic spatial units to eliminate source C errors. These two steps are repeated until all cost estimate error is eliminated in the solution of the problem. Data from the Central Valley of Costa Rica are used to test this demand partitioning method. Specifically, population census data are used to represent demand for services while current health clinics locations are the potential service supply points. The demand partitioning method outperforms current published methods for reducing source C errors. Scope and purpose The objective of the p -median problem is to select p sites from a set of candidate facility sites so as to minimize the cost of servicing a set of existing demand locations or users. However, even specialized heuristics for solving this problem require considerable computational effort for large data sets. Aggregation of the demand locations helps address this difficulty by reducing the problem size but leads to errors in the solution of the problem. An iterative method is introduced in this paper that reduces the error caused by aggregation yet still requires considerably less computational effort than would be needed for the original unaggregated data set.
Year
DOI
Venue
1999
10.1016/S0305-0548(99)00020-9
Computers & OR
Keywords
DocType
Volume
p-median problem,Aggregation error,aggregation error,Location–allocation models,p -median problem
Journal
26
Issue
ISSN
Citations 
10-11
Computers and Operations Research
6
PageRank 
References 
Authors
0.78
0
3
Name
Order
Citations
PageRank
Robert L. Bowerman160.78
Paul H. Calamai248562.07
B. Hall313321.11